“Through my interests in research and patient care, I saw a need for a tool that could streamline a critical gatekeeper process,” said Ella Tetrault, who has worked at Cedars-Sinai for two years and is earning a master of science in health systems at Cedars-Sinai Health Sciences University.

Her tool is designed to save staff time and help patients receive faster access to care by flagging missing details, drafting justification letters, and creating a checklist with action items for staff before they submit the request to insurers.

Tetrault’s tool also is a perfect example of how Cedars-Sinai is empowering employees to use AI to solve common healthcare problems and improve patient care, said Mouneer Odeh, MA, chief data and artificial intelligence officer at Cedars-Sinai.

“We see artificial intelligence as an opportunity to rethink how we deliver care and run our operations,” Odeh said. “Our employees are our first step in identifying how new technologies can become part of our workflows, and their willingness to adopt these technologies drives our innovation.”

During Cedars-Sinai’s “Prompt-A-Thons,” employees compete to develop AI tools to solve operational, clinical and administrative challenges. Nearly 80 groups submitted proposals at the summer competition. Topics ranged from personalizing patient education to assessing patients’ risk for pharmaceutical side effects.

Ten employee teams were then selected to present their AI solutions at a final showcase in front of a panel of judges. Second- and third-place presentations aimed to streamline the Emergency Department trauma documentation auditing process and provide faster, more integrated reporting for medical center data.

During her winning presentation, Tetrault estimated that her pre-authorization tool could save 450 hours per year per authorization staff member.

“According to the American Medical Association, prior authorizations take up roughly 13 hours a week of any given employee’s time,” Tetrault said. “Unfortunately, many of these authorizations are denied due to missing information or submissions not matching the payer’s policy. Most appealed denials get overturned.”

Chain Insights: Turning Prompt-A-Thon Experience Into Action

The Cedars-Sinai Supply Chain Department won a 2025 Prompt-A-Thon for designing an app to help streamline nursing paperwork and give nurses more time for direct patient care. The app, Chain Insights, monitors supplies, tracks back orders, and flags approved substitutes for items that might become unavailable.

Chain Insights, built by a Cedars-Sinai team from Supply Chain Operational Excellence, Enterprise Information Services and Nursing, was first tested as a pilot on an inpatient floor at Cedars-Sinai. Early data shows that it reduced nurses’ supply-ordering time by 1 1/2 minutes per call.

“This time savings adds up to roughly seven hours each day once fully rolled out for the nursing team,” said Nausheen Ahmed, executive director of Supply Chain Operational Excellence. “With approximately 300 calls to our supply chain team each day, this modest but important efficiency means nurses can reclaim time to focus on their most important role, patient care.”

For supply chain staff, knowing how much inventory is available and where it is housed is critical, especially when a nurse calls with a need for supplies. Each call requires a detailed discussion including when the supplies must be in hand and possible substitutions if an item is on back order.

Chain Insights replaces the phone call with an app that precisely locates inventory. The app also identifies unnecessary items to help clear space, a “quieter win” but one that compounds over time.

Regina Chung, MSN, RN, CMSRN, a nurse at Cedars-Sinai, uses Chain Insights every shift.

“Chain Insights has made it easy to quickly order the supplies I need while on the go, even while I am walking in the halls between patients,” Chung said. “I always receive the correct product and quantity, and when items are out of stock, it tells me immediately, so I am not aimlessly waiting. These efficiencies allow me to focus on providing exceptional care to our patients.”

Leaders from Supply Chain Operational Excellence designed data models, decision frameworks and inventory intelligence that tell the tool how to think about supply risk and priorities.

Cedars-Sinai employees from Enterprise Information Services wrote the code and built the infrastructure, turning that back-end logic into a working application. Nursing colleagues provided continuous frontline input, including real-world testing of the tool to ensure it solved the right problems.

“This effort stands as an example of what happens when teams build something together to solve a real problem,” Odeh said. “It's also a signal of what's possible when the people closest to the work are empowered to build the solution, and there's more to come. There is nothing more motivating than seeing knowledge be translated into solutions.”

Cedars-Sinai Health Sciences University is advancing groundbreaking research and educating future leaders in medicine, biomedical sciences and allied health sciences. Learn more about the university.

The findings, published in npj Digital Medicine, suggest this technology could help surgeons refine their techniques and improve patient outcomes.

The AI system, called Frame-to-Outcome (F2O), analyzes video recorded during the nerve-sparing portion of robot-assisted prostate surgery, when surgeons work to preserve the nerves responsible for sexual function. Rather than relying on experts to manually evaluate each procedure, the system automatically identifies patterns in a surgeon’s movements—called “surgical gestures”—and uses them to predict patient recovery.

Prior studies of these surgical gestures demonstrated a strong relationship between the gestures performed by the surgeon—such as the sequence of instruments used or the speed of stretching nerves to move them aside—and the patient’s outcome. By analyzing the gestures used during surgery, the AI system made determinations about whether the patient will be more or less likely to have a good outcome. Until now, this type of analysis required labor-intensive review by trained human observers.

“Our goal isn’t simply to predict who will recover,” said Andrew Hung, MD, corresponding author of the study and professor of Urology at Cedars-Sinai. “We want to identify the surgical techniques with the best outcomes so surgeons can learn, refine and improve care for our future patients.”

Investigators found that F2O matched expert human reviewers in predicting patient outcomes while dramatically reducing the time required to analyze surgical performance. The technology could eventually provide surgeons with objective feedback on the techniques most closely associated with successful patient recovery.

To develop the system, researchers trained the AI system using videos annotated by human analysts from 294 surgeries from 23 surgeons across four international centers. Then they tested it on an additional 29 surgeries and found its predictions closely matched those of expert human reviewers.

“By identifying and interpreting the gestures that result in positive patient outcomes, we can offer surgeons insights that can help improve surgical performance and patient care,” Hung said.

The study was a collaboration between the Department of Urology, the Department of Computational Biomedicine and the Center for Artificial Intelligence Research and Education (CAIRE) at Cedars-Sinai.

“This work highlights what we can achieve when surgeons and computational biomedicine experts work together toward a shared clinical goal," said Jason Moore, PhD, chair of the Cedars-Sinai Department of Computational Biomedicine and director of CAIRE. "Bringing together these disciplines allowed the team to build something that is technically rigorous and genuinely meaningful for surgeons—and their patients."

Additional Cedars-Sinai authors include Xi Li, Nicholas Matsumoto, Jay Moran, Miguel E. Hernandez, Cherine Yang, Jeanine Kim, Jasmine Lin, Peter Wager, Ujjwal Pasupulety and Atharva Deo.

Other authors include Alvin C. Goh, Christian Wagner and Geoffrey A. Sonn.

Funding: Research reported in this publication was supported by the National Cancer Institute of the National Institutes of Health under Award Number R01CA273031. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.

Cedars-Sinai Health Sciences University is advancing groundbreaking research and educating future leaders in medicine, biomedical sciences and allied health sciences. Learn more about the university.

The deployment of OpenEvidence reflects Cedars-Sinai’s broader strategy to use AI tools and technologies to safely improve care precision and produce better patient outcomes.

Through this partnership, Cedars-Sinai physicians, nurses, pharmacists and therapists can ask clinical questions and retrieve medical literature that is relevant to an individual patient’s health profile. The AI tool can help answer clinical questions by linking the latest scientific findings with a patient’s prior procedures, comorbidities, medications, allergies and other health data recorded over time.

Cedars-Sinai also plans to incorporate its own care pathways, protocols and best practices into the OpenEvidence enterprise platform. This will enable Cedars-Sinai clinicians to view current medical literature alongside Cedars-Sinai-specific guidance for delivering safe, high-quality care.

“Integrating OpenEvidence into our electronic health records allows clinicians to look at the latest medical evidence in the context of a patient’s medical history and individual health data, giving physicians and other healthcare professionals a more complete and actionable understanding at the moment of care,” said Shaun Miller, MD, MBA, chief health informatics officer at Cedars-Sinai.

Patient information from the electronic health record will be used only to support care decisions for individual patients and will not be stored by OpenEvidence or used for any other purpose.

“Medicine is not practiced in the abstract. It is practiced on individual patients with unique histories and complexities,” said Daniel Nadler, CEO and founder of OpenEvidence. “This partnership with Cedars-Sinai establishes a new standard: clinical AI that doesn’t just retrieve information but interprets the world’s medical knowledge in the context of the specific patient. We are building a tool that aligns cutting-edge evidence with real-world care to drive better outcomes.”

OpenEvidence is one example of Cedars-Sinai’s broader AI strategy, which aims to streamline and improve patient care, reduce administrative burden on clinicians and educate the workforce about the evolving uses of these technologies.

For example, Cedars-Sinai is using AI to help nurses and other care team members document patient care in real time, analyze and produce reports from echocardiograms, and predict which of two available chemotherapy options for pancreatic cancer would be more effective for an individual patient.

Miller said it is critical to deploy fair, appropriate, effective and safe tools that provide tangible benefits to the roughly 1 million patients treated at Cedars-Sinai each year.

“We want to create a coherent, secure AI ecosystem rather than isolated pilots, and we approach all AI integration carefully,” Miller said.  

Before any AI system goes live at Cedars-Sinai, it goes through a specially designated committee that reviews tools prior to deployment and audits their impact. The committee includes data scientists, clinical experts, administrative leaders and other specialists specific to the particular area under review.

This oversight process is designed to ensure new tools meet Cedars-Sinai’s guidelines for human oversight and verification of AI-generated output, data privacy, and protection of patient health information and intellectual property.

“We see artificial intelligence as an opportunity to rethink how we deliver care and run our operations,” said Mouneer Odeh, MA, chief data and artificial intelligence officer at Cedars-Sinai. “As technologies like OpenEvidence become part of how we work, our strategy centers on improving workflows while supporting the people who do the work.”

AI, Odeh said, is advancing at an incredible pace.

“We are constantly evaluating new technologies with cautious optimism, so we can safely deliver better experiences for our patients and care teams while providing exceptional care to the communities we serve,” he said.  

Cedars-Sinai Health Sciences University is advancing groundbreaking research and educating future leaders in medicine, biomedical sciences and allied health sciences. Learn more about the university.

A follow-up to a successful inaugural event in 2024, the symposium sought to advance the practical deployment of artificial intelligence in healthcare. Attendees from more than 40 institutions explored how to implement AI within clinical settings during the Dec. 12-13 event while addressing critical regulatory and ethical considerations.

“The event was designed to offer a road map to help health systems innovate, implement and integrate AI technologies to improve patient outcomes,” said Sumeet Chugh, MD, vice dean and chief artificial intelligence health research officer at Cedars-Sinai and co-chair of the symposium. “By convening experts from around the world, we created opportunities to exchange ideas, tackle challenges and accelerate the safe and effective use of AI in healthcare.”

The partnership between co-hosts Cedars-Sinai and UHN emphasizes the importance of global collaboration in a rapidly changing technological landscape.

“UHN is proud to partner with Cedars-Sinai and co-host this collaborative international symposium dedicated to advancing artificial intelligence in medicine,” said Barry Rubin, PhD, program medical director of UHN’s Peter Munk Cardiac Centre and symposium co-chair. “Our goal at UHN is to integrate AI and digital tools to improve patient outcomes and enhance the precision, speed and personalization of treatment.”

During a fireside chat, Peter L. Slavin, MD, president and CEO of Cedars-Sinai Health System, and Kevin Smith, PhD, president and CEO of UHN, discussed the need to maintain strong organizational culture while leveraging AI to enhance care delivery.

“In a time when forces are pulling countries and people apart, partnerships like these are essential,” Slavin said.  “AI should not only improve efficiencies but also ensure equity in care and democratize access to information for patients.”

Smith also emphasized his hope for AI to transform care delivery without jeopardizing culture.

“Culture is our bedrock,” Smith said. “We strive to integrate AI into our workflows to improve lives, outcomes and efficiencies—without changing the culture of our unique medical center.”

The event featured panel discussions and debates on pressing topics that included data privacy, the need to train future clinicians on AI documentation tools, and the challenges of data usage and commercialization.

“Provocative topics and debates were purposeful,” Chugh said. “To answer tough questions, we first have to stimulate the conversations that will address these challenges.”

Cedars-Sinai and UHN have committed to reconvening for the third annual symposium in December 2026, continuing their shared mission to lead the global conversation on responsible AI integration in medicine.

Read More From the Cedars-Sinai Newsroom: Cedars-Sinai Strengthens AI Foundation, Accelerates Momentum

As Mouneer Odeh, MA, chief data and artificial intelligence officer at Cedars-Sinai, looks back on the year, he reflects on how the organization deployed AI broadly across clinical, academic and administrative areas to drive clinical and operational excellence and to train the next generation of clinicians, scientists and healthcare professionals.

“AI is not just a tool; it is a catalyst for innovation and transformation,” Odeh said. “Our work this past year centered on translating innovative ideas into solutions with real-world impact and solutions that improve care, enhance discovery and teaching, and create greater efficiencies.”

Odeh said that as he looks at how AI will advance Cedars-Sinai’s work in the year ahead, “our focus for AI is rooted in our mission—elevating the health status of the communities we serve. The pace of innovation is accelerating, and the cumulative impact is growing.”

Under Odeh’s leadership in 2025, the Cedars-Sinai AI Council—a multidisciplinary team comprising clinicians, investigators, data experts and others—helped implement and advance dozens of AI initiatives. These are a few of those achievements.

Delivering Quality Care and Outcomes

Cedars-Sinai was the first health system to launch Aiva Nurse Assistant—initially a pilot program on a 48-bed surgical unit—an artificial intelligence mobile app that allows nurses to use a mobile phone to document patient information in real time through voice dictation.

The app transcribes the data and then—once validated by a clinician—files that information directly into a patient’s electronic medical record. The app is now being deployed across Cedars-Sinai Medical Center with the goal of reducing administrative burdens and fostering increased efficiencies and innovation.   

Another pilot program, Momentum Spine, launched this year at Cedars-Sinai Guerin Children’s for pediatric patients with scoliosis. The monitoring platform enables radiation-free scoliosis assessments and digital models of the patient’s spine. With this real-time tracking of brace wear and scoliosis progression, clinicians have seen an increase in compliance and a reduced need for frequent in-person visits. Cedars-Sinai is planning to expand the program in 2026.

Easing and Expediting Access

The Cedars-Sinai Connect mobile app, launched two years ago, allows Californians to quickly and easily access Cedars-Sinai experts for sick, chronic and preventive care—24/7. In 2025, the AI tool—developed by K Health—expanded to support children and Spanish speakers in the state. While the mobile app brings ease and expedited access to patients, it also reduces administrative burdens like data entry, freeing up more time for clinicians to spend meaningful time with their patients.

The initial success of Cedars-Sinai Connect led investigators to study whether physicians or artificial intelligence offer better treatment recommendations for patients examined through a virtual urgent care setting. A 2025 study published in Annals of Internal Medicine found that initial AI recommendations for common complaints in an urgent care setting were rated higher than final physician recommendations.

While AI was shown to be better at identifying critical red flags, physicians were better at eliciting a more complete history from patients and adapting their recommendations accordingly.

Offering Hands-On Health AI Education

With the launch of Cedars-Sinai Health Sciences University, new training programs in AI, big data and machine learning emerged.

The newly established PhD in Health Artificial Intelligence (AI) program earned accreditation from the Senior College and University Commission of the Western Association of Schools and Colleges. The program is the first in the U.S. to be embedded in a hospital and the first to combine interdisciplinary academic training with hands-on clinical data experience, giving students opportunities to develop AI solutions that could improve diagnostics, patient care and healthcare delivery.

Cedars-Sinai’s National AI Campus—a project-based learning program that brings together AI experts and students from various educational and professional levels—expanded in 2025 to include the first community college, L.A. Pierce College. The initiative addresses challenging problems in science and medicine using AI and machine learning and includes more than 80 partner institutions from 31 states.

Accelerating Research Discoveries

Cedars-Sinai’s research enterprise produced a swath of science in 2025, with dozens of studies focused on advancing the use of artificial intelligence in laboratories and clinics.

These studies included the use of AI to improve mental health therapy, the trending use of agentic AI, how AI can be used to improve drug safety and catch medication risks, potential bias in AI-generated treatments for psychiatric patients, and how the medical center is using synthetic data for clinical innovation.  

Teaching and Training to Ease Administrative Burdens

In 2025, the Enterprise Information Services (EIS) team appointed three key leaders to help implement tools and technologies that can reduce burdens among clinicians: a chief health informatics officer, chief nursing informatics officer and chief medical informatics officer.

The medical center also named the inaugural vice dean and chief artificial intelligence health research officer, who will oversee the newly created AI in Medicine Research Center. The center works collaboratively with the Research section of Enterprise Information Services to build and grow the clinical AI research innovation engine.

Through events like the Cedars-Sinai Prompt-A-Thon, employees worked alongside technical coaches and created their own custom AI prototypes, then optimized the solutions through several iterations of improvements and developed roadmaps for expanding the tools for broader uses.

Additionally, more than 1,000 colleagues were trained on AI prompting, and more than 6,000 users have opted in to use Cedars-Sinai’s proprietary GPT platform. These administrative opportunities aim to encourage the broad use of AI to better reduce inefficiencies and administrative burdens.  

Read more in Cedars-Sinai Discoveries: Cedars-Sinai Pioneers a New Era for AI Education

Synthetic data platforms use artificial intelligence to produce new data that mimics real-world patient data, without disclosing any private information. This approach allows organizations like Cedars-Sinai to simulate various scenarios and outcomes, providing valuable insights for medical research and clinical decision-making, while still maintaining a high level of accuracy and validity in research findings.

For example, Cedars-Sinai can take real patient data and convert it into a new dataset that reflects patient profiles and treatment scenarios. This synthetic dataset could be generated within one hour, offering a significant advantage in speed and efficiency over traditional methods that require time-consuming processes to retrieve real patient data.

“The use of synthetic data at Cedars-Sinai reflects our pursuit of cutting-edge technologies to advance medical research and improve patient care,” said Craig Kwiatkowski, PharmD, senior vice president and chief information officer at Cedars-Sinai. “This supports our broader strategy of building a more connected data ecosystem—one that gives our teams easier access to a wider range of data that helps drive real-world innovation in healthcare.”

To conduct this work, Cedars-Sinai is partnering with Syntho, an Amsterdam-based company that participated in the Cedars-Sinai Accelerator program in 2022. Syntho provides artificial intelligence-based, privacy-enhancing technology to generate anonymous synthetic data.

Cedars-Sinai also is exploring the use of synthetic data in tandem with the recent launch of a new Digital Innovation Platform, as it develops a comprehensive data platform tool set to address some of the most pressing challenges in healthcare. The initiative will leverage Cedars-Sinai’s clinical expertise, infrastructure, research capabilities and vast data resources to develop companies that tackle healthcare issues, in partnership with Cedars-Sinai staff, investors and venture-builder Redesign Health.

Jason Moore, PhD, chair of the Department of Computational Biomedicine, and Nicholas Tatonetti, PhD, vice chair of Computational Biomedicine, are leading research efforts on synthetic data at Cedars-Sinai. They sat down with the Cedars-Sinai Newsroom to share more about the role of synthetic data in a healthcare setting.

What are the benefits of using synthetic data?

Moore: Synthetic data is generated using artificial intelligence to model various patterns in real data and produce new data that preserves these patterns. This synthetic data does not have the same privacy and security issues as real data, making it easier—and much faster—to use for research. That’s because synthetic data doesn’t require approval from committees or governing bodies like an internal review board.

Synthetic data also makes it easier to collaborate and share information with other internal teams and external institutions.

Tatonetti: The speed, accuracy and sheer volume in which we can access synthetic data opens the door to studying new and complex conditions like rare diseases.  

What is the difference between de-identified data and synthetic data?  

Moore: De-identified data is real data with patient identifiers removed, while synthetic data is completely artificial. This artificial data is generated to preserve the relationships and patterns in the original data, making it useful for research without the privacy concerns associated with real data.

What about the privacy and security of synthetic data?   

Moore: One of the biggest motivators of using synthetic data is to ensure patient privacy and security. Synthetic data eliminates the possibility of re-identifying patients, thus allowing researchers to work with data without the same restrictions as real data.

How will the partnership with Syntho advance our work with synthetic data?

Tatonetti: Through our partnership with Syntho, we hope to achieve three things:

• Lower the barriers to clinical research, allowing more investigators to conduct studies without lengthy approval processes associated with real data
• Speed up the process of launching and dropping studies, enabling researchers to quickly test and iterate on their hypotheses
• Allow students and trainees in the Cedars-Sinai Health Sciences University to use synthetic data, providing them with realistic datasets to learn from, build tools on, and conduct analyses.

What are the limitations of synthetic data?

Moore: Synthetic data has limitations and does not handle all data types well—like discrete genetic data—so it’s imperative we understand these limitations in our workflows. It’s also critical we effectively communicate these limitations to our users to prevent user frustration and fatigue.

Cedars-Sinai Health Sciences University is advancing groundbreaking research and educating future leaders in medicine, biomedical sciences and allied health sciences. Learn more about the university.

(HSU). They are the first to enroll in the new Chuck Lorre Allied Health School, Health Artificial Intelligence PhD program in the Graduate School of Biomedical Sciences and the Master of Science in Regenerative Medicine program.

“Regardless of their chosen discipline, students in the Health Sciences University will be immersed in the healthcare environment and our clinical care continuums,” said Jeffrey Golden, MD, executive vice dean of Research and Education, director of the Burns and Allen Research Institute, and the Linda and Jim Lippman Distinguished Chair in Academic Medicine at Cedars-Sinai. “Students will train side by side with experts to advance basic, translational and clinical sciences, then witness how their efforts help shape the care Cedars-Sinai delivers.”

The students’ experience will include hands-on training from prominent experts and access to advanced technologies and innovative clinical trials.

The Cedars-Sinai Chuck Lorre Allied Health School was established in 2022 with a $30 million gift from The Chuck Lorre Family Foundation. The vocational school offers training in allied healthcare roles, starting with clinical laboratory scientists, pharmacy technicians, respiratory therapy and radiation therapy technicians.

“When the opportunity presented itself to provide training and certificates for underserved individuals in our community, which in some instances would double their salaries, I was all in,” Lorre said. “Partnering with Cedars-Sinai to create the school of allied health will allow us to see long-term impacts in our communities.”

Students in the allied health programs will receive hands-on training in a clinical setting through rotations at Cedars-Sinai and its affiliates Huntington Health and Torrance Memorial Health. The programs lead to associate degrees, bachelor’s degrees or certificates. Graduates are eligible to obtain licensure and take certification exams in their chosen fields.

“The transformational gift made by The Chuck Lorre Family Foundation meets a tremendous need for allied health specialists, both at Cedars-Sinai and in the broader Los Angeles community,” Golden said.

Alexia Furbert, a 21-year-old Los Angeles resident, is one of the first students to enroll in the pharmacy technician program in the Cedars-Sinai Chuck Lorre Allied Health School.

“When I learned about the program, I was finishing my last semester at West Los Angeles College to complete my associate degree,” Furbert said. “Feeling uncertain about my career goals, I questioned whether transferring to a four-year university was the right decision for me. This program offered an opportunity to explore potential career paths and contribute to chronically understaffed areas of healthcare.”

Also new to the Cedars-Sinai Health Sciences University is the PhD in Health Artificial Intelligence (AI), which offers rigorous training in AI algorithms and methods, with a focus on analyzing clinical data to enhance patient care.

“We are elated to welcome our incoming students, who will experience a hands‑on, active approach to teaching that reinforces AI concepts through clinical rotations and scholarly collaboration with physicians and medical staff,” said Graciela Gonzalez-Hernandez, PhD, director of the Graduate Program in Artificial Intelligence. “Graduates will be positioned to directly improve healthcare and patient outcomes through the rigorous development and deployment of AI algorithms and software.”

Another new offering is the Master of Science in Regenerative Medicine, a 20-month program where students focus on stem cell research that can be used to both model and treat human diseases. The curriculum will focus on three professional paths: cell biomanufacturing, academic research into stem cell biology and learning about how stem cells can be used with different clinical specialties.

“We designed the master’s program to teach students about regenerative medicine and how stem cells hold an interesting promise for medicine,” said Wafa Tawackoli, PhD, director of Education and Training at the Board of Governors Regenerative Medicine Institute. “We are focused on giving these students an advantage as they move forward in their chosen career paths through a carefully designed curriculum. Everything we do is tailored to the future.”

The university offers other graduate degrees, including a PhD in Biomedical Sciences, that merges scientific and translational medicine curricula with mentoring by researchers and clinicians, a Master of Science in Health Systems and a Master of Science in Magnetic Resonance in Medicine.

The university also is home to several professional training programs, including nondegree educational certifications, formal trainings, internships and other ongoing opportunities to benefit students and professionals at all levels of their careers.

“We are eager to welcome our new and returning students,” Golden said, “and for them to begin their journey of understanding human diseases, how to diagnose them and how to determine the best treatments for individual patients.”

Cedars-Sinai Health Sciences University is advancing groundbreaking research and educating future leaders in medicine, biomedical sciences and allied health sciences. Learn more about the university.

Investigators studied four large language models (LLMs), a category of AI algorithms trained on enormous amounts of data, which enables them to understand and generate human language. In medicine, LLMs are drawing interest for their ability to quickly evaluate and recommend diagnoses and treatments for individual patients.

The study found that the LLMs, when presented with hypothetical clinical cases, often proposed different treatments for psychiatric patients when African American identity was stated or simply implied than for patients for whom race was not indicated. Diagnoses, by comparison, were relatively consistent. The findings were published in the peer-reviewed journal NPJ Digital Medicine.

“Most of the LLMs exhibited some form of bias when dealing with African American patients, at times making dramatically different recommendations for the same psychiatric illness and otherwise identical patient,” said Elias Aboujaoude, MD, MA, director of the Program in Internet, Health and Society in the Department of Biomedical Sciences at Cedars-Sinai and corresponding author of the study. “This bias was most evident in cases of schizophrenia and anxiety.”

Among the disparities the study uncovered were the following:

• Two LLMs omitted medication recommendations for an attention-deficit/hyperactivity disorder case when race was explicitly stated, but they suggested them when those characteristics were missing from the case.
• Another LLM suggested guardianship for depression cases with explicit racial characteristics.
• One LLM showed increased focus on reducing alcohol use in anxiety cases only for patients explicitly identified as African American or who had a common African American name. 

Aboujaoude suggested the LLMs showed racial bias because they reflected bias found in the extensive content used to train them. Future research, he said, should focus on strategies to detect and quantify bias in artificial intelligence platforms and training data, create LLM architecture that resists demographic bias, and establish standardized protocols for clinical bias testing.

“The findings of this important study serve as a call to action for stakeholders across the healthcare ecosystem to ensure that LLM technologies enhance health equity rather than reproduce or worsen existing inequities,” said David Underhill, PhD, chair of the Department of Biomedical Sciences at Cedars-Sinai and the Janis and William Wetsman Family Chair in Inflammatory Bowel Disease. “Until that goal is reached, such systems should be deployed with caution and consideration for how even subtle racial characteristics may affect their judgment.”

Other Cedars-Sinai authors include: Ayoub Bouguettaya, PhD. Other authors include Elizabeth M. Stuart, PhD.

Cedars-Sinai Health Sciences University is advancing groundbreaking research and educating future leaders in medicine, biomedical sciences and allied health sciences. Learn more about the university.

The U.S. healthcare system continues to face significant challenges, including rising costs, workforce shortages, inefficiencies and the growing complexity of patient care. Cedars-Sinai is launching the Digital Innovation Platform to address these challenges by developing and scaling healthcare solutions that are clinically effective, operationally scalable and financially sustainable.

“Cedars-Sinai is uniquely positioned to develop new and innovative ways to fix some of the most pressing problems in healthcare today, thanks to our focus on translational medicine and our ability to bring together leading-edge clinical care, research, AI and strategic industry relationships to benefit our patients,” said Peter L. Slavin, MD, president and CEO of Cedars-Sinai and the David and Meredith Kaplan Presidential Chair. “This bold approach and unique ecosystem will allow us to address healthcare challenges on a deeper level and move faster by collaborating with key partners to develop innovations that have been shown to work in real-world, clinical environments.”

Cedars-Sinai will leverage the scalable innovation model created by Redesign Health, a venture builder that has successfully launched more than 60 healthcare companies in the past seven years. Together, they will develop a sustainable innovation center by tapping the resources and network of an academic healthcare system. With this new platform, they hope to nurture a digital innovation ecosystem in Los Angeles to improve the delivery of high-value healthcare.

The Digital Innovation Platform will source external talent—healthcare business strategists, engineers, data scientists and others—who will leverage Cedars-Sinai’s internal clinical expertise and emerging technology to develop and scale new digital health solutions that address critical needs within the healthcare system. Cedars-Sinai is striving to identify solutions for personalized medicine, specialty care access, hospital workflow optimization, clinical decision support, and increased coordination among patients, providers and payers.

Cedars-Sinai clinicians, researchers and staff members will have the opportunity to participate in a new, structured entrepreneurship program to identify challenges in their everyday work that could be addressed through the Digital Innovation Platform. The program will focus on fostering a culture of entrepreneurship among staff members while providing guidance and mentorship to help them better recognize digital innovation opportunities. The newly formed companies will likewise benefit from the clinical expertise of Cedars-Sinai’s staff.

The Digital Innovation Platform will source experienced founders to lead the new companies, and Cedars-Sinai will be their first large-scale customer to validate solutions. Founders will gain access to an integrated data platform Cedars-Sinai is developing that uses deidentified, secure and synthetic data—artificial data that mimics real-world patterns. This enterprise tool will provide access to large, diverse datasets supplying critical infrastructure and insights. The data will facilitate the use of AI and other emerging technologies during the design of digital solutions while supporting development at every stage of company growth.

Founders will fine tune these digital solutions in a variety of real-world clinical settings at Cedars-Sinai, benefitting from the organization’s large network of providers, sites of care and strategic partnerships.

“We’re pleased to partner with Cedars-Sinai to launch transformative healthcare companies, combining our experience launching healthcare companies with their exceptional clinical expertise and longstanding dedication to driving innovation in healthcare,” said Brett Shaheen, founder and CEO of Redesign Health. “By harnessing Cedars-Sinai’s groundbreaking medical research, expansive provider network and clinical insights, we’re uniquely positioned to develop transformative businesses that not only address unmet needs but also shape the future of healthcare on a global scale.”

The partnership will lay the foundation for a sustainable model that brings together ideas, funding and clinical expertise to address some of healthcare’s most critical challenges through digital innovation.

“With the Digital Innovation Platform, we hope to catalyze a broader healthcare innovation ecosystem in Los Angeles—connecting universities, entrepreneurs, investors and other healthcare leaders to foster a thriving hub for digital health innovation,” Slavin said.

Read more on the Newsroom: AI Enhances Scoliosis Monitoring for Pediatric Patients

The late-breaking study presented at the American College of Physicians Internal Medicine Meeting and published simultaneously in the Annals of Internal Medicine compared initial AI treatment recommendations to final recommendations of physicians who had access to the AI recommendations but may or may not have reviewed them.

“We found that initial AI recommendations for common complaints in an urgent care setting were rated higher than final physician recommendations,” said Joshua Pevnick, MD, MSHS, co-director of the Cedars-Sinai Division of Informatics, associate professor of Medicine and co-senior author of the study. “Artificial intelligence, as an example, was especially successful in flagging urinary tract infections potentially caused by antibiotic-resistant bacteria and suggesting a culture be ordered before prescribing medications.”

However, Pevnick said that while AI was shown to be better at identifying critical red flags, “physicians were better at eliciting a more complete history from patients and adapting their recommendations accordingly.”

The retrospective study was conducted using data from Cedars-Sinai Connect, a virtual primary and urgent care program that began in 2023. An extension of Cedars-Sinai’s in-person care, Cedars-Sinai Connect aims to expand virtual healthcare for patients in California through a mobile app that allows individuals to quickly and easily access Cedars-Sinai experts for acute, chronic and preventive care.

The study reviewed 461 physician-managed visits with AI recommendations from June 12 through July 14, 2024. Key medical issues addressed during these virtual urgent care visits involved adults with respiratory, urinary, vaginal, vision or dental symptoms.

Patients using the mobile app initiate visits by entering their medical concerns and, for first-time users, providing demographic information. An expert AI model conducts a structured dynamic interview, gathering symptom information and medical history. On average, patients answer 25 questions in five minutes.

An algorithm uses the patient’s answers as well as data from the patient's electronic health record to provide initial information about conditions with related symptoms. After presenting patients with possible diagnoses to explain their symptoms, the mobile app allows patients to initiate a video visit with a physician.

The algorithm also suggests diagnosis and treatment recommendations that can be viewed by the Cedars-Sinai Connect treating physician, though during the time of the study, Cedars-Sinai Connect required physicians to scroll down to view them.

“The major uncertainty of this study is whether physicians scrolled down to view the prescribing, ordering, referral or other management suggestions made by AI, and whether they incorporated these recommendations into their clinical decision-making,” said Caroline Goldzweig, MD, Cedars-Sinai Medical Network chief medical officer and co-senior author of the study. “The fact that the AI recommendations were often rated as higher quality than physician decisions, however, suggests that AI decision support, when implemented effectively at the point of care, has the potential to improve clinical decision-making for common and acute conditions.”

The AI system used for Cedars-Sinai Connect is developed by K Health, which created the technology to reduce the burdens of clinical intake and data entry, allowing doctors to focus more on patient care. K Health and Cedars-Sinai developed Cedars-Sinai Connect through a joint venture and collaborated on the research study. Investigators from Tel Aviv University, including first author Dan Zeltzer, PhD, also participated in the study.

“We put AI to the test in real-world conditions, not contrived scenarios,” said Ran Shaul, co-founder and chief product officer of K Health. “In the reality of everyday primary care, there are so many variables and factors—you’re dealing with complex human beings, and any given AI has to deal with incomplete data and a very diverse set of patients.”

Shaul said the investigators learned that if you train the AI on the treasure trove of de-identified clinical notes and use day-to-day provider care as an always-on reinforcement learning mechanism, “you can reach the level of accuracy you would expect from a human doctor.”

Other authors involved in the study include Dan Zeltzer, PhD; Zehavi Kugler, MD; Lior Hayat, MD; Tamar Brufman, MD; Ran Ilan Ber, PhD; Keren Leibovich, PhD; Tom Beer, MSc; and Ilan Frank, MSc.

This work was supported with funding by K Health.

Cedars-Sinai Health Sciences University is advancing groundbreaking research and educating future leaders in medicine, biomedical sciences and allied health sciences. Learn more about the university.

The hottest trend on the horizon for artificial intelligence (AI) is agentic AI, according to

Unlike traditional AI that is primarily designed to complete a single task, agentic AI is a new generation of AI models that can independently perform multiple tasks simultaneously to achieve specific objectives.

At Cedars-Sinai, Moore and colleagues are immersed in agentic AI models, creating algorithms that make faster and more accurate decisions while combing through large datasets. Moore, professor of Computational Biomedicine and Medicine, sat down with the Cedars-Sinai Newsroom to explain the potential—and recent boom—in the use of agentic AI.

What is agentic AI and how does it differ from existing AI models?

For the past 10 years we have been developing state-of-the-art AI methods, including deep-learning algorithms and large language models for natural language processing. These methods have been designed to complete one specific task—for example, to analyze an echocardiogram image of the heart to find defects.

Agentic AI, however, assembles teams of AI specialists to complete specific tasks—then collectively brings these teams together to solve a complex problem. Using the same echocardiogram example, an agentic AI model can simultaneously analyze echocardiogram images, laboratory tests, vital signs, medication history and clinical notes to provide a comprehensive picture of a patient in a fraction of the time it would take multiple clinicians to review results.

This technique mirrors the way humans solve complex problems.  

What makes agentic AI the next hottest trend in AI?

ChatGPT has shown we can use powerful algorithms for specific tasks. With agentic AI, algorithms are tailored to specific needs and adapts strategies independently to achieve predefined goals. It can assemble teams of AI agents to handle various tasks.

In my laboratory, for example, we work with big data. So, we need people whose expertise is in cleaning data, preparing it for analysis, building computational models with the data and providing statistical analysis. We also need people who can interpret the data for us; what does the data tell us about biology, clinical care, etc.? We then need someone to summarize all of these results in written form, then prepare graphs and figures to communicate these results.

Agentic AI builds teams of AI agents that handle each of these respective areas, with the end goal of providing understanding of the data and explanation of the results.

The field is advancing in a way that individuals may soon use these methods at home. I expect to see many tools coming out in the next year or so that will make our lives easier. One can imagine an AI agent helping prepare your taxes, your family budget, or preparing your weekly grocery list.

Is there published research happening in agentic AI?

Yes, we are seeing an uptick in published research studies involving agentic AI. Our laboratory recently published a study in Bioinformatics about an agentic AI model we created called ESCARGOT (Enhanced Strategy and Cypher-driven Analysis and Reasoning using Graph Of Thoughts).

The ESCARGOT model combines large language models with a dynamic “graph of thoughts” and biomedical knowledge graphs—an approach that was shown to improve output reliability and reduce inaccuracies. To do this, we inputted existing data we have procured about Alzheimer’s disease, then asked the agentic AI model to provide several things: genes associated with the disease, drugs and therapies that may offer the best treatments for these genetic variations, etc.

We compared these findings to the responses ChatGPT produced and, not shockingly, agentic AI provided answers with 80%-90% accuracy, compared to ChatGPT, which scored about 50%.

We believe strongly in making our models open-access to ensure science progresses. The ESCARGOT model is public, free and available on GitHub.

Cedars-Sinai Health Sciences University is advancing groundbreaking research and educating future leaders in medicine, biomedical sciences and allied health sciences. Learn more about the university.

The findings, published in the peer-reviewed journal Med, demonstrate how AI can improve drug safety, support drug discovery and improve understanding of medication risks.

The database is called OnSIDES (ON-label SIDE effectS resource). It is free and publicly available on GitHub.

“OnSIDES provides the most comprehensive and up-to-date database of adverse drug events from drug labels,” said Nicholas Tatonetti, PhD, vice chair of Computational Biomedicine at Cedars-Sinai and corresponding author of the study. “This work enables researchers and clinicians to systematically study drug safety.” 

Adverse drug events are unintended, harmful events related to the usage of medication and are the fifth leading cause of death internationally. Experts believe half of all adverse drug events are preventable.

“While many drug safety studies are conducted on individual medications during clinical trials and through post-marketing surveillance programs, far fewer studies have studied the occurrence of adverse drug events more broadly,” said Tatonetti, also the associate director for Computational Oncology at Cedars-Sinai Cancer. “The lack of broadscale studies may be attributed in part to the array of medications and the complexity of drug interactions, as well as the lack of standardized data publicly available.”

The OnSIDES model analyzed 3,233 unique drug ingredient combinations extracted from 47,211 labels and identified over 3.6 million pairs of medications and adverse drug events. This work was also expanded to labels from countries outside of the U.S., revealing differences in how adverse drug events are reported internationally.

By using artificial intelligence to extract adverse drug events from drug labels, investigators improved access to structured, machine-readable data, ultimately making it easier to identify drug risks, predict new drug uses and enhance patient safety.

“This resource supports drug repurposing, pharmacovigilance, and AI-driven drug discovery,” said Jason Moore, PhD, chair of the Department of Computational Biomedicine at Cedars-Sinai. “We are hopeful that future research can build on OnSIDES to develop better predictive models, personalized medicine approaches and regulatory insights, ultimately leading to safer medications and more informed clinical decision-making worldwide.”

Additional Cedars-Sinai authors include Apoorva Srinivasan, Michael Zietz, Gaurav Sirdeshmukh and Jacob Berkowitz.

Additional authors include Yutaro Tanaka, Hsin Yi Chen, Pietro Belloni, Undina Gisladottir, Jenna Kefeli, Jason Patterson and Kathleen LaRow Brown.

Funding: R35GM131905 to N.P.T, T32GM145440 to H.Y.C, T15LM007079 to U.G, M.Z, K.L.B.

Cedars-Sinai Health Sciences University is advancing groundbreaking research and educating future leaders in medicine, biomedical sciences and allied health sciences. Learn more about the university.

Momentum Spine, a graduate of the Cedars-Sinai Accelerator program, is an AI, mobile spine-imaging and monitoring platform. The platform personalizes the monitoring of spinal deformities while reducing the lifelong consequences of repeated radiation exposure.

“This technology is a true game changer, allowing many of our patients outside Southern California to receive radiation-free scoliosis assessments without the need for travel,” said David Skaggs, MD, co-director of Cedars-Sinai Spine, executive vice chair of the Department of Orthopaedics and director of Pediatric Orthopaedics at Cedars-Sinai Guerin Children's. “Scoliosis can progress rapidly in growing children; this tool allows us to monitor changes closely and intervene early when needed.”

The pilot program, launched at Cedars-Sinai Guerin Children’s, centers on Momentum Spine’s AI-driven, 3D-scanning technology, which enables radiation-free scoliosis assessments and digital models of the patient’s spine.

Parents can utilize the Momentum Spine application on their mobile phones to record a 45-second video of their child standing still while the parent walks around their child. This video is then automatically converted into a 3D model and analyzed by AI to determine the Cobb angle—a measurement used by healthcare providers—to determine the degree of spinal curvature in conditions like scoliosis. Both the 3D model and the analysis results are shared with the care team through a secure web portal. 

Patients also get electronic sensors that are fitted into the custom-fitted back braces. Through the advanced sensor technology and AI-driven, 3D analytics, clinicians can optimize treatment strategies while providing personalized feedback to patients and their families.

Traditionally, physicians rely on in-person visits, X-rays and self-reported brace compliance to treat scoliosis patients.

"Momentum Spine empowers parents to partner with their child’s doctor by providing real-time tracking of brace wear and scoliosis progression,” said Elaine Butterworth, RN, pediatric nurse navigator and the director of Patient Experience and Education for the Pediatric Spine Program. “By increasing compliance and reducing the need for frequent in-person visits, this technology enhances both patient care and peace of mind for families."

Patients also benefit from reduced travel—especially for those in rural areas. The app also enables closer monitoring and earlier intervention when necessary.

“Cedars-Sinai is the first health system to integrate Momentum Spine with both 3D scanning and brace sensors into its pediatric clinics,” said Philippe Miller, CEO of Momentum Health. “This collaboration is a significant step forward in spinal health, as we share the same goal—to ensure every child receives the best possible scoliosis care.”

Read more on the Cedars-Sinai Blog: Seeking a Second Opinion for a Spine Condition

An artificial intelligence (AI) program can identify chronic liver disease from videos taken during a common heart test known as an echocardiogram, Cedars-Sinai investigators report.  

“Incorporating AI into echocardiograms, which capture images of the heart and the liver, can lead to a diagnosis of liver disease without additional costs,” said Alan Kwan, MD, assistant professor in the Department of Cardiology in the Smidt Heart Institute at Cedars-Sinai, and senior and corresponding author of the study, published in NEJM AI.

Echocardiography is the use of ultrasound to visual the heart and its associated structures. The imaging test is a common early screening test doctors prescribe if they suspect a patient has cardiovascular disease. A standard echocardiogram study often contains more than 50 videos, of which a few typically include images of the liver.  

“People with heart disease often develop chronic liver disease, distinguishing between primary liver disease and liver injury secondary to heart disease can be challenging,” said David Ouyang, MD, a cardiologist in the Department of Cardiology in the Smidt Heart Institute, an investigator in the Division of Artificial Intelligence in Medicine, and a senior author of the study. “Our deep-learning model can help doctors spot liver disease that might have gone unnoticed and thus direct appropriate follow-up testing.”

An estimated 4.5 million people have been diagnosed with liver disease, according to the U.S. Centers for Disease Control and Prevention. But experts with the American Liver Foundation say many more people likely have undiagnosed steatotic liver disease, the condition formerly called fatty liver disease.  

“These novel findings exemplify how AI models are helping us to augment clinical diagnostics at a body-systems level instead of just individual organs,” said Sumeet Chugh, MD, director of the Division of Artificial Intelligence in Medicine at Cedars-Sinai.

Investigators trained an AI program to study patterns in more than 1.5 million echocardiogram videos. The program, EchoNet-Liver, was able to detect cirrhosis (scarring of the liver) or steatotic liver disease by studying images of the liver picked up during the echocardiograms. The technology builds upon EchoNet, technology developed by Ouyang and colleagues that can identify and analyze patterns in echocardiograms.

The investigators compared the deep-learning model’s predictions with diagnoses made using patients’ abdominal ultrasounds or MRI images. The AI program proved similar to detecting liver disease by these traditional imaging studies read by radiologists.

The study authors said their next step is to test EchoNet-Liver in studies that track patients’ health over time.

Other Cedars-Sinai authors include Yuki Sahashi, MD, MSc; Milos Vukadinovic, BS; Hirsh Trivedi, MD^; Susan Cheng MD, MMSc, MPH. Other authors include Fatemeh Amrollahi PhD; Justin Rhee, BS; and Jonathan Chen, MD, PhD.

Disclosure of interest: YS reports support from the KAKENHI (Japan Society for the Promotion of Science: 24K10526), oversea research grant from SUNRISElab, Japan Heart Foundation and Ogawa Foundation and honoraria for lectures from m3.com inc. DO reports support from the National Institutes of Health (NIH; NHLBI R00HL157421 and R01HL173526) and Alexion, and consulting or honoraria for lectures from EchoIQ, Ultromics, Pfizer, InVision, the Korean Society of Echocardiography, and the Japanese Society of Echocardiography. ACK reports consulting fees from InVision and support from the American Heart Association (AHA; 23CDA1053659) and National Institutes of Health (NIH; UL1TR001881).

Cedars-Sinai Health Sciences University is advancing groundbreaking research and educating future leaders in medicine, biomedical sciences and allied health sciences. Learn more about the university.

The app allows nurses to use a mobile phone to document patient information in real time through voice dictation. It transcribes the data and then—once validated by a clinician—the app files that information directly into the patient’s electronic medical record.

“This AI-driven technology furthers our commitment to our incredible and dedicated nursing staff by reducing administrative burdens, allowing for increased efficiencies and innovation,” said Craig Kwiatkowski, PharmD, senior vice president and chief information officer at Cedars-Sinai. “As we consider advancing this pilot program, we hope to make the day-to-day role of our nurses easier and more efficient, building on the progress we have made with our physicians.”

Currently being tested by nurses and clinical partners on a 48-bed surgical unit, the pilot expands upon similar technologies already in use by Cedars-Sinai physicians.

“Cedars-Sinai was the first health system to launch Aiva Nurse Assistant,” said Sumeet Bhatia, Aiva founder and CEO. “We’re fortunate to work with an organization so relentlessly focused on the wellbeing of their front line care team.”

Rachel Coren, MPH, MS, vice president and associate chief information officer at Cedars-Sinai, worked alongside Peachy Hain, MSN, RN, Cedars-Sinai's executive director of Nursing, Surgical Services and Clinical Support Programs, and their teams to roll out the Aiva pilot program. They sat down with the Cedars-Sinai Newsroom to share more about the potential of this AI advance.  

How does Aiva Assistant help nurses in their daily workflow?

Hain: Nurses juggle multiple responsibilities, from patient care to complex documentation and care coordination. Studies show they spend up to 40% of their shift on documentation alone, contributing to burnout and staffing challenges.

We asked ourselves: What if AI could support nurses just as it has supported physicians? With that vision in mind, we partnered with our Enterprise Information Services team and Aiva Health—part of the Cedars-Sinai Accelerator Program since 2017—to bring this idea to life. The result was the Aiva Nurse Assistant, a tool designed by nurses, for nurses, to ease documentation and give them more time for patient care.

The goal with Aiva Nurse Assistant is simple—reduce administrative tasks so nurses can focus on patient care. By streamlining documentation, we’re giving nurses back valuable time, improving efficiency and ultimately enhancing the patient experience.

How does the Aiva Nurse Assistant app work?

Coren: Nurses use hospital-issued iPhones with the HIPAA-compliant mobile app powered by conversational AI, allowing them to enter data real-time into 50 of the most commonly used fields in Epic—a widely used electronic health record (EHR) system—using voice or text.

The first step is to press and hold the talk button at the bottom of the app and dictate their observation. For example, “The patient in Room 8915 has a pain level of 3 in her back and also finished eating 50% of her lunch.”

The app then transcribes the input and maps the data to the appropriate rows in the EHR. Before submission, clinicians confirm the patient information and nurse observations presented are accurate. If accurate, they then click 'accept’ and all data is directly input into the EHR.

A single command can document across fields and flow sheets from anywhere in the hospital, streamlining the process further and saving valuable time.

How is the app different from other AI tools Cedars-Sinai has implemented?

Coren: What’s novel is that we are using AI in nursing documentation workflows and we are one of the first hospitals to move this into a real-world setting.

Nursing is an ever-evolving profession with increasing documentation requirements, and this platform is built to grow alongside it. Many AI and voice-enabled technologies in healthcare have focused on reducing physician burnout and streamlining their workflows. With the Aiva Nurse Assistant pilot, we wanted to bring that same level of support to nurses and care teams—giving them a tool designed specifically for their unique needs.

Can you share any early feedback from nurses about their experience using Aiva Nurse Assistant?

Hain: The feedback has been overwhelmingly positive—it's not just helpful, it’s impactful. Even our most experienced nurses, some with over 40 years at Cedars-Sinai, have embraced this technology. Many who were initially hesitant about AI now say it has significantly reduced their documentation time, and the instant output has lifted a major burden.

As the pilot unit integrates Aiva into daily workflows, we’re not just analyzing the numbers—we’re listening to our nurses and clinical partners.

Cedars-Sinai nurses have always been at the forefront of innovation, continuously adapting to new tools that enhance their workflows—nurses like Ryan Trias, MSN, RN, who played a key role in integrating Aiva into daily practice, helping to refine its functionality to meet real-world nursing needs. His collaboration with the team ensured that the technology aligns with how nurses work, making adoption smoother.

One nurse laughed about how his teammates kept asking how he was finishing his documentation so quickly—they were amazed at how efficient he had become. Another nurse called Aiva Assistant "a lifesaver," which perfectly sums up its impact on their workflow. 

Their insights and experiences are at the core of our evaluation because they’re the ones delivering care on the front lines. Their feedback determines whether this technology is truly making a difference.

What are the next steps for the Aiva Assistant pilot?

Coren: At Cedars-Sinai we have a long history of introducing new technologies in close partnership with our clinical users. Our goal is to gather feedback around usability and adoption and to continue to measure the impact it has on reducing the administrative burden on our clinical staff.

We're then going to be working very hard to rapidly expand its utilization to as many inpatient units as possible here at Cedars-Sinai and at our affiliate hospitals. We're also beginning to work closely with other disciplines, like pharmacy, to define use cases for their teams and begin to develop implementation plans around those.

How do you see AI evolving in nursing care, and what role will Cedars-Sinai play in shaping that future?

Hain: We’re exploring how Aiva Assistant may help streamline other nursing tasks to enhance patient care through voice-activated task reminders, lab results retrieval, remote control of in-room devices like TVs, and breaking down language barriers.

The current pilot has revealed the exciting potential of how technology can redefine the way care is delivered. Throughout this trial, the emphasis is on fostering more meaningful patient interactions, enhancing charting efficiency and—most importantly—creating a more fulfilling experience for both staff and patients.

Coren: Nurses at Cedars-Sinai are innovative and tend to be early adopters. We are currently working on designing our hospital room of the future, which we know will include lots of new technologies, some of which will be voice enabled, allowing our patients to fully immerse in their experience and have more control over both their room-request health education and entertainment on demand, while they continue to heal with us.

Read more from Discoveries Magazine: Cedars-Sinai Pioneers a New Era for AI Education

“Pathologists traditionally identify relevant structures in diseased tissue by visually examining stained tissue images—a method that is costly, time-consuming and typically reliant on a single type of tissue imaging,” said Kyle Coleman, PhD, an investigator in the Department of Computational Biomedicine at Cedars-Sinai, who led the team that developed the MISO tool. “MISO streamlines this process by integrating detailed molecular information with tissue histology, enabling automated and precise identification of disease-relevant structures.”  

The MISO tool can, for example, distinguish regions with different levels of cancer severity within a single colon cancer tissue sample. Additionally, by integrating transcriptomics, metabolomics, and histology imaging, MISO accurately maps detailed structures of the mouse hippocampus at a high resolution. Investigators hope that MISO can lead to a deeper understanding of disease processes, potentially advancing the development of new therapies.

The MISO software is currently available on GitHub.

Additional authors include Amelia Schroeder, Melanie Loth, Daiwei Zhang, Jeong Hwan Park, Ji-Youn

Sung, Niklas Blank, Alexis J. Cowan, Xuyu Qian, Jianfeng Chen, Jiahui Jiang, Hanying Yan, Laith Z. Samarah, Jean R. Clemenceau, Inyeop Jang, Minji Kim, Isabel Barnfather, Joshua D. Rabinowitz, Yanxiang Deng, Edward B. Lee, Alexander Lazar, Jianjun Gao, Emma E. Furth, Tae Hyun Hwang, Linghua Wang, Christoph A. Thaiss, Jian Hu and Mingyao Li.

Funding: This research was supported by National Institutes of Health grants R01HG013185 and R01LM014592.

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Mouneer Odeh, MA, has been named the inaugural chief data and artificial intelligence officer for Cedars-Sinai Health System. In this pivotal role, Odeh will advance Cedars-Sinai’s data analytics and artificial intelligence (AI) initiatives, aligning them with the organization’s broader digital and information technology strategy.

“Mouneer is a proven change agent with more than 25 years of leadership experience in data analytics, data science, health information and related specialties,” said Craig Kwiatkowski, PharmD, senior vice president and chief information officer at Cedars-Sinai. “We are excited to support Mouneer as he builds upon our existing data analytics environment and AI capabilities, strengthening our systemwide strategy and charting a new course through rapid evolution of this exciting field.”

Odeh will lead enterprise-wide efforts to harness data analytics and AI to drive innovation across care delivery and administrative functions. He also will oversee a diverse team of professionals spanning advanced analytics, research, infrastructure, governance, data science and business intelligence.

This inaugural role will also create collaborations with varying leaders across the broader Cedars-Sinai Health System to foster a data-driven culture, manage the life cycle of analytics and AI solutions, and enhance data and AI governance and policy to align with the health system’s strategic goals.

Most recently, Odeh served as vice president of Analytics at Inova Health System in Fairfax, Virginia, where he was responsible for leading the analytics operating model to drive clinical and business transformation with strategic impact. Under his leadership, Inova became the first health system in the U.S. to achieve The Joint Commission's Responsible Use of Health Data Certification, recognizing ethical and transparent practices in data use.

Previously, Odeh served as vice president of Enterprise Analytics and the chief data scientist at Thomas Jefferson University and Jefferson Health in Philadelphia, where he established enterprise functions to support strategic growth. 

Earlier in his career he held various leadership roles at Quest Diagnostics, including director of Health Information Ventures, where he launched innovative information startup businesses to develop and commercialize clinical informatics, population health and personalized medicine. His experience also includes roles in analytics, sales and marketing in various commercial firms.

Odeh earned his bachelor’s degree in economics from McGill University and his master’s degree in economics from Georgetown University. 

“The responsible use of data and AI will not only improve patient care, but also accelerate clinical research, streamline operations and enhance the experiences of our patients and healthcare teams,” Odeh said. “Cedars-Sinai is a leader leveraging these technologies to transform healthcare, and I look forward to the lasting impact our work will have in advancing our mission to elevate the health status of the communities we serve.”

Read More From the Cedars-Sinai Blog: What You Should Know About AI in Medicine

The program’s findings, published in Circulation, may help clinicians identify patients whose mitral valve regurgitation is manageable with medication as well as patients with more severe cases who would benefit from a minimally invasive valve repair procedure or surgery.

“Mitral regurgitation is a common but often missed valvular heart disease. It can be challenging to precisely assess the disease severity, which is critical to know which patients can take a watch-and-wait approach and which should proceed to an intervention,” said David Ouyang, MD, a cardiologist in the Department of Cardiology in the Smidt Heart Institute, an investigator in the Division of Artificial Intelligence in Medicine, and corresponding author of the study. “The program we developed may one day be used by doctors when considering the best treatment approach for individual patients.”

The AI program further cements the Smidt Heart Institute’s longstanding leadership in heart valve care. Interventionalists with the Smidt Heart Institute are believed to have performed more mitral valve repairs than any other center in the U.S., with outcomes that place Cedars-Sinai among the top-performing programs nationally. The Smidt Heart Institute team has also completed more than 1,500 robotic mitral valve repairs with a near 100% success rates.

“This could improve how we identify patients with mitral regurgitation, which is becoming more prevalent in our aging population, and to personalize treatment even more so than we already do,” said Raj Makkar, MD, associate director of the Smidt Heart Institute, vice president of Cardiovascular Innovation and Intervention for Cedars-Sinai and a leader in treating mitral valve disease.

The heart has four valves that open and close to move blood throughout the body. In some people the mitral valve, located on the left side of the heart, does not close properly, which allows blood to flow backwards, a condition called mitral valve regurgitation. The condition prevents enough blood from circulating throughout the body and, over time, can lead to shortness of breath, arrhythmia and heart failure.

“At Cedars-Sinai we are pursuing the use of AI as a complementary tool in diagnosing and treating conditions such as mitral valve regurgitation,” said Sumeet Chugh, MD, director of the Division of Artificial Intelligence in Medicine and the Pauline and Harold Price Chair in Cardiac Electrophysiology Research.

In developing the new program, investigators used more than 58,000 transthoracic echocardiograms from Cedars-Sinai. Echocardiograms are video images of patients’ hearts taken by ultrasound and is the most common way to assess mitral regurgitation. The investigators tested the program on echocardiograms from 1,800 patients at Cedars-Sinai as well on echocardiograms from 915 patients from Stanford Healthcare in Northern California.

The model was able to automatically identify moderate and severe mitral valve regurgitation with high precision.

“Our deep learning model analyzed videos from more than 50,000 echocardiogram studies and can pinpoint the most relevant and important videos to assess mitral regurgitation severity,” said first author Amey Vrudhula, a fellow at Cedars-Sinai.

To treat severe mitral valve regurgitation, experts at the Smidt Heart Institute at Cedars-Sinai rely on either the minimally invasive TEER procedure or minimally invasive surgery. All patients meet with an interventional cardiologist as well as a cardiac surgeon before making their treatment decision.

Other Cedars-Sinai authors involved in the study include Grant Duffy B.S., Milos Vukadinovic B.S^., Susan Cheng, M.D., M.M.Sc., M.P.H.

This work was supported in part by the Sarnoff Cardiovascular Research Award, research grants NIH R01-HL131532, NIH R01-HL142983, R00 HL157421, and R01HL173526.

Read more from the Cedars-Sinai Blog: Treatment Options for Heart Valve Disease

The study detailed how the tool, a free and publicly available software platform, analyzes and compiles data and information—including new peer-reviewed studies—to answer researchers’ queries. The key to the tool’s success is a new type of large language model, said Jason H. Moore, PhD, professor and chair of the Department of Computational Biomedicine at Cedars-Sinai and senior and corresponding author of the study.

Large language models are a specific type of AI programs that can distill large amounts of data—like medical studies, books, articles and interviews—and use that data to create new content.

“The large language model approach we developed uses knowledge stored in a special database, called a knowledge graph, that specializes in capturing the relationships between entities such as drugs and genes,” Moore said.

Historically, the main challenge in using large language models to generate content is ensuring quality, accuracy and reliability of the generated responses.

The Cedars-Sinai technique, however, moved past this challenge by using the graph-of-thoughts technique—a framework that allows investigators to break down a problem into subproblems, and turn the information generated by the large language models into a visual graph.  

The Cedars-Sinai tool also incorporates retrieval augmented generation, or RAG, which augments large language models with external data sources that provide relevant facts and context. Together, this powerful tool unearths efficient and accurate data and information about varying conditions and diseases, including Alzheimer’s disease, which was the focus of the research study published in Bioinformatics.

The open-source software, called Knowledge Retrieval Augmented Generation ENgine—or KRAGEN—is publicly available on GitHub, a cloud-based platform that helps developers collaborate and manage code. To date, the software has received more than 400 endorsements from users.

To demonstrate the usability of the database, Moore and team used KRAGEN to generate data on Alzheimer’s disease, including data on genes, drugs and other aspects related to the condition. Investigators asked the database questions like, “What drugs bind to the proteins APOE and PTAU?” And “Which are genes associated with Alzheimer’s disease?”

Instead of receiving a list of data points for their question, investigators received a synthesized summary of information.

“This AI approach is a step toward fully automating the analysis of Alzheimer’s disease data by incorporating knowledge generated from previous biomedical research studies,” Moore said.

As a next step, investigators are working on ways to integrate KRAGEN into Cedars-Sinai’s AI software for automated machine learning analysis of complex biomedical data.

“This advance is a big step toward allowing users to issue spoken commands to perform analyses in minutes, that otherwise could take weeks or months,” said Craig Kwiatkowski, PharmD, senior vice president and chief information officer at Cedars-Sinai, who was not involved in the study. “It’s encouraging to see the potential of this tool to impact AI-driven programs at Cedars-Sinai.”

Other authors involved in the study include Nicholas Matsumoto, Jay Moran, Hyunjun Choi, Miguel E. Hernandez, Mythreye Venkatesan, and Paul Wang.

This work is supported in part by funds from the Center for AI Research and Education at Cedars-Sinai Medical Center and grants from the National Institutes of Health USA (U01 AG066833 and R01 LM010098).

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The design and evaluation of EchoCLIP, described in a manuscript published in the peer-reviewed journal Nature Medicine, suggest that an EchoCLIP interpretation of a patient’s echocardiogram provides clinician-level evaluations of heart function, assessment of past surgeries and devices, and may assist clinicians in identifying patients in need of treatment. The EchoCLIP foundation model also can identify the same patient across multiple videos, studies and timepoints as well as recognize clinically important changes in a patient’s heart. 

“To our knowledge, this is the largest model trained on echocardiography images,” said corresponding author David Ouyang, MD, a faculty member in the Department of Cardiology in the Smidt Heart Institute and in the Division of Artificial Intelligence in Medicine. “Many previous AI models for echocardiograms are only trained on tens of thousands of examples. In contrast, EchoCLIP’s uniquely strong performance in image interpretation is a result of its training on almost tenfold more data than existing models.”

“Our results suggest that large datasets of medical imaging and expert-adjudicated interpretations can serve as the basis for training medical foundation models, which are a form of generative artificial intelligence,” Ouyang said. He said this advanced foundation model can soon help cardiologists in the assessment of echocardiograms by generating preliminary assessments of cardiac measurements, identify changes that happen over time, and common disease states.

The team of investigators built a dataset of 1,032,975 cardiac ultrasound videos and corresponding expert interpretations to develop EchoCLIP. Key takeaways from the study include:

• EchoCLIP displayed strong performance when assessing cardiac function using heart images.
• The foundation model could identify implanted intracardiac devices like a pacemaker, implanted mitral valve repairs and aortic valves from the echocardiogram images.
• EchoCLIP accurately identified unique patients across studies, identified clinically important changes such as having undergone heart surgery, and enabled the development of a preliminary text interpretation of echocardiogram images.

“Foundation models are one of the newest areas within generative AI, but most models do not have enough medical data to be useful in the healthcare arena,” said Christine M. Albert, MD, MPH, chair of the Department of Cardiology in the Smidt Heart Institute and the Lee and Harold Kapelovitz Distinguished Chair in Cardiology.

Albert, who was not involved in the Nature Medicine study, said, “This novel foundation model integrates computer vision interpretation of echocardiogram images with natural language processing to augment cardiologists’ interpretation of echocardiograms.” 

Cedars-Sinai investigator and first author Kai Christensen was also involved in the study. Other authors involved in the research include Milos Vukadinovic and Neal Yuan.

Ouyang is funded by NIH NHLBI grant R00HL157421.

Read more from the Cedars-Sinai Blog: The Human Factor of Artificial Intelligence  

The findings were published in the peer-reviewed journal Nature Communications.

“These results are likely practice-changing for many patients because this technology can accurately identify cardiovascular risk without the use of invasive tests or contrast dye that some patients cannot receive,” said Piotr J. Slomka, PhD, director of Innovation in Imaging at Cedars-Sinai, professor of Medicine in the Division of Artificial Intelligence in Medicine and senior author of the study.  

Slomka, a professor of Cardiology in the Department of Cardiology in the Smidt Heart Institute at Cedars-Sinai, said that more than 15 million CT scans are performed in the U.S. each year— and many of these are underutilized or understudied. Clinicians currently can evaluate cardiovascular risk using a CT scan, but usually with contrast.

“This novel AI algorithm makes it possible to get crucial heart health insights from cheaper scans that use less radiation, potentially making detailed heart evaluations part of regular diagnostic procedures,” Slomka said.

Investigators incorporated two artificial intelligence models to evaluate data on coronary calcium and heart muscle chamber sizes from nearly 30,000 patient imaging records. They were able to determine that those measures are a better indicator of cardiac risk than a radiologist’s identification of abnormalities.

Sumeet Chugh, MD, director of the Division of Artificial Intelligence in Medicine, who was not involved in the study, says this technology allows for large-scale use of existing CT data to spot individuals at risk sooner.

“Coronary artery disease is the leading cause of disability and death at a global level,” said Chugh, associate director of the Smidt Heart Institute. “These findings highlight how AI tools could leverage existing CT images performed for lung disease investigation, to make a cost-effective, public health impact on heart disease.”

The collective group of Cedars-Sinai investigators that developed these findings also included teams from the Smidt Heart Institute, the Biomedical Imaging Research Institute and the Department of Biomedical Sciences.

Other Cedars-Sinai authors include Robert J. H. Miller, Aditya Killekar, Aakash Shanbhag, Bryan Bednarski, Anna M. Michalowska, Mark Lemley, Konrad Pieszko, Serge D. Van Kriekinge, Paul B. Kavanagh, Joanna X. Liang, Cathleen Huang, Damini Dey and Daniel S. Berman. Other investigators include Terrence D. Ruddy, Andrew J. Einstein and David E. Newby.

This research was supported in part by grant R35HL161195 from the National Heart, Lung, and Blood Institute/National Institutes of Health (NHLBI/NIH) (PI: PS) as well as R01EB034586 from the National Institute of Biomedical Imaging and Bioengineering (PI: PS). The authors thank the National Cancer Institute for access to NCI’s data collected by the National Lung Screening Trial (NLST) accessed under project number NLST-981. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.

Read more from the Cedars-Sinai Blog: An AI Ally for Mental Health

Cedars-Sinai has joined a White House initiative aimed at ensuring healthcare providers and companies use artificial intelligence (AI) ethically and responsibly. Together with the U.S. Department of Health and Human Services, the AI effort—announced in late 2023—is a consortium involving nearly 40 health systems and insurers.

“Cedars-Sinai is honored to be part of this collective commitment to responsible AI use in healthcare, acknowledged by the White House,” said Craig Kwiatkowski, PharmD, senior vice president and chief information officer at Cedars-Sinai. “This pledge reflects our dedication to principles that prioritize fairness, appropriateness, validity, effectiveness and safety in leveraging AI for scientific discoveries and improved patient care.”

As described by the White House, members’ voluntary commitments reflect a series of actions that underscore three principles that are fundamental: safety, security and trust.

In joining the initiative, Cedars-Sinai committed to:

• Vigorously develop AI solutions to optimize healthcare delivery and payment by advancing health equity, expanding access, making healthcare more affordable, improving outcomes through more coordinated care, improving patient experience, and reducing clinician burnout.
• Ensure outcomes are aligned with fair, appropriate, valid, effective and safe AI principles—called “FAVES” in AI circles.
• Deploy “trust mechanisms” that inform users if content is largely AI-generated and not reviewed or edited by a human. 
• Adhere to a risk management framework that includes comprehensive tracking of applications powered by frontier models and an accounting for potential harms and steps to mitigate them.
• Research, investigate, and swiftly develop AI solutions, but do so responsibly.

These commitments align with Cedars-Sinai’s AI strategy, which is led by its AI Council. The AI Council brings together cross-functional leaders—from patient care, research, data and technology teams—to review, guide and coordinate the health system’s AI strategy. The council provides a forum for open dialogue and an ongoing exchange of ideas while setting institutional and system priorities, evaluating the use of AI tools and identifying measures of success.

Jason Moore, PhD, a founding member of the AI Council and chair of the Department of Computational Biomedicine at Cedars-Sinai, says the White House initiative affirms Cedars-Sinai’s commitment to the ethical, responsible and scientifically sound use of AI.

“As one of the early adopters of artificial intelligence and machine learning in healthcare, Cedars-Sinai ensures our endeavors are not only conducted ethically and responsibly, but also significantly enhance the lives of our patients,” Moore said. “This White House initiative is another tool in our arsenal to ensure that our research and applied uses are innovative, patient-centric and long lasting.”   

In addition to the White House Initiative on Healthcare AI, Cedars-Sinai recently joined the Trustworthy & Responsible AI Network, or TRAIN, governance network. Led by Microsoft, this network of healthcare institutions aims to put responsible AI guidelines into practice.

Through this consortium, the collective whole will align on operationalizing responsible AI principles to improve the quality, safety, and trustworthiness of AI in healthcare. Participants will share best practices and be provided with tools to enable measurement of outcomes associated with the implementation of AI.

Kwiatkowski said Cedars-Sinai will work with the network to advance internal tools and validation processes as the first step, and then look to build upon that via network collaborations, federated sharing models, and an AI outcomes registry.

“These projects and collaborations are critical to our ongoing efforts at Cedars-Sinai, and broadly within healthcare, as we continue to explore new tools and use cases,” said Kwiatkowski.

Read more from Discoveries: Artificial Intelligence Advances

Jason Moore, PhD, an expert in artificial intelligence and professor and chair of the Department of Computational Biomedicine, sat down with the Cedars-Sinai Newsroom to discuss how the team draws on applied mathematics, bioengineering, biomedical informatics, biostatistics and computer science to answer biomedical and clinical research questions.

How do you define computational biomedicine?

Put simply, those who work in computational biomedicine at Cedars-Sinai are interested in improving the lives of our patients by using computers and computing technologies along with data resources.

To go a level deeper, our department uses leading-edge and state-of-the-art methods and algorithms in AI and machine learning for the analysis of clinical data. We partner with clinicians to embark on research projects, and we participate in clinical rotations to ensure our work is most meaningful to the patients and community we serve.

What excites you most about this booming field?

My entire career has been spent in AI, and the most exciting time is right now. Artificial intelligence has matured to the point where it’s both useful and practical, both clinically and for research. For the first time, we can think seriously about putting AI in the clinic to improve patient care and clinical decision-making.

At Cedars-Sinai specifically, our computational biomedicine capabilities are exceptional, thanks to the infrastructure built by our colleagues in Enterprise Information Services (EIS). This infrastructure—coupled with our collaborative efforts among their employees—has created a culture that is accepting of utilizing AI where it’s most beneficial, safe and effective.   

What sets Cedars-Sinai’s computational biomedicine team apart?

We have recruited some of the best professionals in the country, many of whom were interested in embedding themselves within a hospital with direct opportunities to impact clinical care. We also have a strong academic and training component, and our graduate students serve as the glue to bring collaborators together. 

Cedars-Sinai Health System is especially unique because we work alongside one another to improve patient care. Patient care is our end goal; that’s our bottom line. Silos go out the door, innovation is expedited and building bridges among departments is understood as critical.

The Department of Computational Biomedicine team has many experts and areas of focus. Tell us about some of the people and projects within the department.  

• Jesse Meyer, PhD, an assistant professor in the Department of Computational Biomedicine and a research scientist in the Smidt Heart Institute, is providing computational tools that make data analysis possible. He is working alongside Jennifer Van Eyk, PhD, director of the Advanced Clinical Biosystems Institute in the Smidt Heart Institute and a world leader in clinical proteomics, which is the study of the structure and function of proteins within the body.  
  
  While Van Eyk works to bring mass spectrometry—the way we measure proteins—to every patient at Cedars-Sinai, Meyer is developing the computational methods for processing that data. These tools can infer what proteins are present within a sample, then use AI and machine learning to analyze that data. Meyer is also exploring how—and when—proteomics can integrate with clinical data to better predict clinical outcomes.
   
• Graciela Gonzalez-Hernandez, PhD, vice chair of Research and Education in the Department of Computational Biomedicine, is a pioneer in utilizing artificial intelligence for natural language processing. She is on the front lines of using large language models to mine clinical notes, the published literature, and social media data to address key questions in health research.
  
  For example, if individuals are having an adverse reaction to a commonly prescribed medication, they may share their experience online through social media. Gonzalez-Hernandez would collect that online data, create a repository for it, then mine that information for clinically relevant takeaways.
   
• In medical school, trainees learn the “if, then” rule. If a patient is older than 65 years old, then you should check what medications they take. We take the same “if, then” approach in computational biomedicine, but call it rule-based machine learning. The concept is that we build machine learning models from data, then teach the technology “rules” that are intended for clinicians to interpret.
  
  Ryan Urbanowicz, PhD, a research assistant professor on our team, is one of the world’s experts in rule-based machine learning. He has developed powerful systems that mine for data, then present clinical findings in an immediate, and explainable way, for clinicians. 
   
• Automated machine learning is a keen interest of mine and a specialty where we translate data into usable information. At Cedars-Sinai, we are collecting a tremendous amount of data in proteomics, genomics and beyond.
  
  Our experts, including Kyoung Jae Won, PhD, and Nicholas Tatonetti, PhD, integrate computational methods, databases, machine learning and AI to provide translational, clinical information. The end goal with our work is to do a better job of understanding diseases and helping patients make more informed decisions.  
   
• Tiffani Bright, PhD, a national leader in applied clinical informatics, serves as co-director of the Center for Artificial Intelligence Research and Education within the Department of Computational Biomedicine. She is spearheading the development of new AI algorithms and software, and applying those findings into genomic research, personalized medicine and other healthcare research applications. Bright's work ensures our team is diverse, equitable and inclusive in its research and education programs, making certain that innovative solutions are accessible and relevant to all communities.
   
• The primary roles within our department wouldn’t be possible without our biostatisticians, whose complementary job function is critical to our work. Under the leadership of Mourad Tighiouart, PhD, director of the Biostatistics Core and Biostatistics Research Center, these team members apply math and statistics to answer some of the biggest questions at Cedars-Sinai and in the broader healthcare landscape. As a cancer research scientist, Tighiouart ensures our computational biomedicine team collaborates and communicates with the cancer enterprise at Cedars-Sinai—which expedites novel discoveries in the laboratory.

Read more on the Cedars-Sinai Blog: Dr. Tiffani Bright | AI Evangelist and Skeptic

Another is studying how artificial intelligence (AI) technology can help predict disease outcomes through X-rays, CT scans and MRI images.

A third group hopes to predict COVID-19 outbreaks using genomic data that machine learning algorithms understand.

This is a sampling of the eight projects that more than 170 undergraduate and graduate students, postdoctoral students, scientists, medical residents, faculty members and others working in scientific or medical institutions are participating in this spring through Cedars-Sinai’s National AI Campus. This project-based learning initiative is in its second year at Cedars-Sinai, and it brings together AI experts and people from various educational and professional levels to address challenging problems in science and medicine using AI and machine learning.

National AI Campus is part of the Center for Artificial Intelligence Research and Education, a component of the medical center’s Department of Computational Biomedicine. It was developed in 2018 by Xiuzhen Huang, PhD, research professor in Computational Biomedicine, when she was at Arkansas State University. When Huang joined Cedars-Sinai, she brought National AI Campus along. Today, National AI Campus is connected to a nationwide program with participants from diverse academic programs and institutions and also includes high school students.

“National AI Campus makes artificial intelligence accessible to a broad community by offering a collaborative, highly interactive training program in which everyone can learn from each other,” Huang said. “At Cedars-Sinai, because of our focus as a medical center and academic institution, we tailor our program around biomedically related projects. We offer medical imaging and genomic projects, as well as those focusing on business analytics and social sciences.”

National AI Campus is free of charge and open to anyone working at Cedars-Sinai or at other invited institutions—at any experience level, in any degree or area of specialty, and with or without previous programming, machine learning or high-performance computing experience.

“All you need is a willingness to learn the basics and an interest in exploring the leading edge of AI and machine learning in medicine,” said Professor Jason Moore, PhD, chair of the Department of Computational Biomedicine, director of the Center for Artificial Intelligence Research and Education and chair of the National AI Campus Steering Committee.

“Artificial intelligence has great potential to improve modern life by addressing many of society’s major challenges, particularly those related to human health. At Cedars-Sinai, our ultimate goal is to one day move our findings into the clinic to help patients.”  

AI makes it possible for computers to perform tasks that require human intelligence. Machine learning is a subset of artificial intelligence in the computer science field. It gives computers the ability to “learn” with data, without being explicitly programmed, and it recognizes patterns in large amounts and diverse types of data to generate important insights.

There are two phases of the National Campus AI program, and each phase lasts four months. To ensure each phase is successful, Huang enlisted Ryan Urbanowicz, PhD, a research assistant professor in the Department of Computational Biomedicine, to serve as director of the campus program, and Joshua Levy, PhD, director of Digital Pathology Research, as associate director.

During Phase One, small interdisciplinary teams of up to 20 people work individually and as a group—online and on their own time—on a project. The teams are led and mentored by experts who have experience in each project topic. Phase One culminates with a showcase of team presentations, including to the Cedars-Sinai research community. This year’s showcase is planned for the summer.

During an optional Phase Two of the program, participants are selected to be part of a global AI competition or a novel Cedars-Sinai-based collaborative research project aiming toward publication in a peer-reviewed medical journal. Cedars-Sinai’s National AI Campus teams won Phase Two competitions in 2023 and have been recognized nationally and internationally.

More than 50 universities across the U.S. also participate in National AI Campus, including 21 historically Black colleges and universities. Cedars-Sinai experts are working to expand the program to California universities and are helping California State University at Dominguez Hills start its own National AI Campus program.

One participant in Cedars-Sinai’s spring 2024 National AI Campus program kickoff said she works in research administration. She noted that she was eager to learn ways to use AI to make data analysis more precise and efficient in her job and to learn skills she could use in other ways.

This feedback aligns with another goal of National AI Campus, Huang said: to create a strong educational resource for students and faculty members and enhance workforce development in AI.

“AI technology is impacting our lives in a major way,” Huang said. “I compare it to when the steam engine came along 300 years ago, bringing dramatic change and transforming industry. AI is another powerful invention that is making significant changes, and although there is some societal anxiety around how to safely use it, I always emphasize that AI will change our life, but it will never replace our life.

“It’s important that Cedars-Sinai harnesses the potential that AI and machine learning offer, and one way we are doing that is through National AI Campus.”

Read more on the Cedars-Sinai Blog: Physician Shadowing Program Offers Students Rare Access

could help physician-scientists who obtain patient consent to extract information from these patients’ pathology reports for research and clinical trial recruitment.

“Cancer is a complex disease, and rich information is contained in the notes that a pathologist makes when they review a patient’s cancer underneath the microscope,” said Nicholas Tatonetti, PhD, vice chair of Operations in the Department of Computational Biomedicine at Cedars-Sinai, associate director of Computational Oncology at Cedars-Sinai Cancer and senior author of the study. “But because these notes are in the form of scanned PDFs, the text they contain has been inaccessible to computers—until now.”

To create a machine-readable pathology dataset, Tatonetti and his team worked with The Cancer Genome Atlas, a publicly available collection of information from thousands of U.S. cancer patients who have given permission for investigators to examine their personal health records.

“The pathology reports in the atlas are scanned in at all angles and in different formats from each of the institutions that provided them,” Tatonetti said. “They’re messy and their scan quality is relatively poor—not unlike pathology forms you would find in patient records.”

Investigators used AI to clean up the scans so that optical character recognition software could turn them into machine-readable notes. When investigators compared these notes against the original reports, they found this method was highly accurate.

“By making the reports machine readable, we can train algorithms to extract information from them in response to investigators’ questions,” Tatonetti said. “This will help investigators identify and validate new disease markers, conduct research, and recruit patients for clinical trials.”

The resulting collection of text from the reports, now publicly available, includes data on almost 10,000 cancer patients. The format is commonly used in machine learning to allow computational biologists and computer scientists to use the data, Tatonetti said. The method could also be used to extract pathology report data from other datasets.

“The true story of a patient’s condition, such as detailed information about their cancer and the effects of various therapies, is found in clinicians’ notes,” said Cedars-Sinai Cancer Director Dan Theodorescu, MD, PhD, the PHASE ONE Foundation Distinguished Chair and Director at the Samuel Oschin Comprehensive Cancer Institute. “Tools that help us mine this information further our efforts to conduct translational studies that bring the promise of precision medicine to each of our patients.”

Tatonetti and his team are now focused on training models to extract specific information—such as cancer staging—from the data.

“Our model can extract that information when it is present in the notes, but it can also accurately infer the stage when it is not explicitly stated,” Tatonetti said. “For instance, the pathologist might make a note about a secondary lesion or about or evaluating a sample of a breast cancer from the liver. These notes don’t include the word metastatic, but they do imply it.”

The team is also working to apply its method to the Molecular Twin Precision Oncology Platform, a unique precision medicine and AI tool created at Cedars-Sinai that includes pathology reports and other data on the majority of Cedars-Sinai’s cancer patients. Team members are also developing tools to make other clinician notes from patient records machine readable, Tatonetti said.

“AI enhancements to optical character recognition are the key to extracting a wealth of data from some of the most clinically relevant portions of patient records,” said Jason Moore, PhD, chair of the Department of Computational Biomedicine at Cedars-Sinai. “This data will fuel new studies by researchers across specialties, including research clinicians, clinical trial investigators and investigators working to improve tools that allow computers to interpret clinical language.”

Funding: This work was supported by National Institute of General Medical Sciences of the National Institutes of Health grant number R35GM131905.

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“Sudden cardiac arrest is a mostly lethal condition, and prevention will make the biggest impact, but we need to find novel clinical tools to make that possible,” said Sumeet Chugh, MD, director of the Division of Artificial Intelligence in Medicine at Cedars-Sinai and senior author of both studies. “Using AI algorithms to improve prediction of sudden cardiac arrest could help doctors identify which patients might be at higher risk of experiencing this devastating condition.”

More than 350,000 people have an out-of-hospital sudden cardiac arrest in the United States every year, according to the Centers for Disease Control and Prevention.

During sudden cardiac arrest, a change in the heart’s electrical activity causes it to suddenly stop beating. Having a heart condition can make a person more likely to experience sudden cardiac arrest, but it also can occur in people with no known heart condition.  

In a study published in Communications Medicine, David Ouyang, MD, assistant professor of Cardiology and Medicine at Cedars-Sinai, along with Chugh and fellow investigators trained a deep learning algorithm to study patterns in electrocardiograms, also known as ECGs, which are recordings of the heart’s electrical activity.

The model studied electrocardiograms from people who experienced sudden cardiac arrest and people who did not. The study included 1,827 pre-cardiac arrest electrocardiograms from 1,796 people who later experienced sudden cardiac arrest. It also included 1,342 electrocardiograms taken from 1,325 people who did not experience sudden cardiac arrest. 

The investigators found the Cedars-Sinai-developed AI model more accurately predicted who would experience out-of-hospital sudden cardiac arrest than did the more conventional method, called the ECG risk score. This is a way for doctors to calculate a person’s risk for sudden cardiac arrest that incorporates information from electrocardiogram readings.

“The entire digital electrocardiogram signal performed significantly better than a few of its components,” said Chugh, who is also the Pauline and Harold Price Chair in Cardiac Electrophysiology Research and associate director in the Smidt Heart Institute. “We plan to continue to study this AI method to learn how it could be used in a clinical setting.”

In another study, published in Circulation: Arrhythmia and Electrophysiology, Chugh along with fellow investigator Piotr Slomka, PhD, director of Innovation in Imaging at Cedars-Sinai and a research scientist in the Division of Artificial Intelligence in Medicine and the Smidt Heart Institute; and other colleagues trained an AI model to differentiate between two underlying causes of sudden cardiac arrest: pulseless electrical activity and ventricular fibrillation.

Pulseless electrical activity means that the heart’s electrical signals are too weak to produce a heartbeat. It cannot be treated with a defibrillator and often leads to death. Ventricular fibrillation is a type of irregular heartbeat that can cause the heart to stop beating, but an electric shock from a defibrillator can trigger the beating again. 

After the AI model reviewed patterns in electrocardiogram readings as well as patient characteristics, investigators were able to determine risk factors for both types of sudden cardiac arrest.

People who had pulseless electrical activity sudden cardiac arrest, for example, were more likely to have been older, been overweight, have had anemia, or experienced shortness of breath as a warning symptom. Those who had ventricular fibrillation were more likely to be younger, have had coronary artery disease or experienced chest pain as a warning symptom.

“We have ways of preventing sudden cardiac arrest through technologies like a defibrillator, but the challenge is knowing who is most likely to benefit from this intervention,” said Lauri Holmstrom, MD, PhD, a visiting postdoctoral scientist at Cedars-Sinai and first author of both studies. “These findings could help cardiologists identify which patients are likely to have a pulseless electrical activity sudden cardiac arrest or ventricular fibrillation sudden cardiac arrest, and help them prevent these events from occurring.”

The AI models used in both studies were trained, tested, and validated using data from two ongoing studies of sudden cardiac arrest founded and led by Chugh: the Oregon Sudden Unexpected Death Study and the Ventura Prediction of Sudden Death in Multi-Ethnic Communities (PRESTO) study.

“These studies exemplify the potential for AI to detect patterns in the body that the human eye and standard medical tests cannot,” said Paul Noble, MD, the Vera and Paul Guerin Family Distinguished Chair in Pulmonary Medicine and chair of the Department of Medicine at Cedars-Sinai, who was not involved in the studies. “We are getting closer to being able to use AI to prevent dangerous events such as sudden cardiac arrest.”

Other Cedars-Sinai investigators who worked on the Communications Medicine study include Harpriya Chugh; Kotoka Nakamura, PhD; Ziana Bhanji; Madison Seifer; Audrey Uy-Evanado, MD; and Kyndaron Reinier, PhD.

Other Cedars-Sinai investigators who worked on the Circulation: Arrhythmia and Electrophysiology study include Bryan Bednarski; Harpriya Chugh; Habiba Aziz; Hoang Nhat Pham, MD; Arayik Sargsyan, MD; Audrey Uy-Evanado, MD; Damini Dey, PhD; and Kyndaron Reinier, PhD.

Funding: Both studies were funded, in part, by the National Heart, Lung, and Blood Institute.

Read more on the Cedars-Sinai Blog: Heart Attack, Cardiac Arrest, Heart Failure—What's the Difference?

The application—called Xaia, for eXtended-Reality Artificially Intelligent Ally—expands access to mental health support for patients, furthering Cedars-Sinai’s mission to elevate the health of communities it serves in Los Angeles and beyond.

Cedars-Sinai investigators created Xaia as a way to offer patients self-administered, AI-enabled, conversational therapy in relaxing spatial environments such as a creek-side meadow or a sunny beach retreat where patients also can do deep breathing exercises and meditation.

The Xaia application offers users an immersive therapy session led by a trained digital avatar, programmed to simulate a human therapist.

“Apple Vision Pro offers a gateway into Xaia’s world of immersive, interactive behavioral health support—making strides that I can only describe as a quantum leap beyond previous technologies,” said Brennan Spiegel, MD, MSHS, professor of Medicine, director of Health Services Research at Cedars-Sinai and co-founder of the Xaia technology. “With Xaia and the stunning display in Apple Vision Pro, we are able to leverage every pixel of that remarkable resolution and the full spectrum of vivid colors to craft a form of immersive therapy that’s engaging and deeply personal.”

While developing the app for Apple Vision Pro, Spiegel said, his Cedars-Sinai team reimagined how spatial computing can support behavioral health and overall wellbeing in ways never before possible.

Omer Liran, MD, a psychiatrist at Cedars-Sinai and co-founder of Xaia, says the app represents a transformative step in making quality therapy accessible to all.

“Apple Vision Pro has allowed us to create a platform where technology fades into the background and the user's healing journey comes to the forefront,” Liran said. “This application is a culmination of years of rigorous research, clinical expertise and a vision to democratize mental wellness in a way that respects the uniqueness of every individual's experience. Using the powerful capabilities of Vision Pro, we’re now able to bring this experience to life with its full potential.”

Before the launch of Apple Vision Pro, the Xaia technology was featured in a first-of-its-kind study published in the peer-reviewed journal Nature Digital Medicine. Findings from the study showed Xaia as both beneficial and safe for patient use.

To develop Xaia, Spiegel and Liran enlisted the support of Cedars-Sinai’s Technology Ventures—the enterprise that protects and supports the commercialization of discoveries and technologies. Technology Ventures also facilitates access to promising inventions—like Xaia—that improve the quality of life for patients around the world.

The managing director of Cedars-Sinai’s Technology Ventures, Nirdesh Gupta, PhD, said that ideas developed in an academic medical center environment like Cedars-Sinai offer a one-of-a-kind perspective—focused both on advancing research and expediting clinical care needs.

James Laur, JD, vice president of Intellectual Property at Cedars-Sinai, echoed that sentiment.

“The hospital ecosystem is at the heart of care delivery, and Cedars-Sinai has the unique advantage of being closest to the patients and their varying needs,” said Laur. “Xaia is the result of several years of support and funding, and we are elated to see the technology come to life in this meaningful and clinically relevant way.”

Conflicts of Interest: Spiegel and Liran are co-founders of VRx Health. The Xaia technology is exclusively licensed from Cedars-Sinai to VRx Health for commercialization.

Read more from the Cedars-Sinai Blog: Virtual Reality and the Brain-Body Connection

The findings, published in the peer-reviewed journal Nature Communications, suggest that more was in play than either factor alone.  

“Our results suggest that initially, differences in shared home environment influenced who was infected with COVID-19 more than genetic differences,” said Katie LaRow Brown, MA, first author of the study and a PhD candidate at Columbia University who collaborated with Cedars-Sinai on this study. “Over time, however, the importance of these differences in shared home environment decreased—and the importance of genetics increased—eventually eclipsing shared home environment.”

COVID-19 has infected more than 340 million people in the U.S., underscoring the urgency in conducting therapeutic research and uncovering potential treatments. However, until this study, little was known about how an individual’s environment and genetic background impacted their experience with the virus.

Using electronic health records from New York-Presbyterian/Columbia University Irving Medical Center, investigators identified 12,764 patients who received conclusive results—either positive or negative—from a PCR test for COVID-19. These patients belonged to 5,676 families with an average of 2.5 family members who had a bout of COVID-19. The time frame studied was Feb. 21, 2020, to Oct. 24, 2021.

The investigators’ analysis found that at the start of the pandemic, genetics accounted for 33% of variation in susceptibility. By the second half of the research study, however, genetics accounted for 70% of variation in susceptibility.

When measuring patients’ severity of COVID-19, investigators also found that a patient’s genetics were more of a factor than their home environment. Disease severity was defined by length of hospital stay. Genetics explained 41% of variation while shared environment explained 33%.  

“We were especially surprised by the percentages of susceptibility,” said Nicholas Tatonetti, PhD, senior and corresponding author of the study, vice chair of Operations in the Department of Computational Biomedicine and an associate director of Computational Oncology at Cedars-Sinai Cancer. “Since this is an infectious disease, we assumed that home environment differences would explain most variation for the entirety of the study.”

While Tatonetti says his team of investigators cannot know for certain, they suspect that over time, discrepancies between people’s home environments changed in important ways.

“This work also suggests that the specific genetic factors influencing susceptibility and severity have not been fully identified,” said Tatonetti. “This is very important in terms of directing resources and defining future research goals.”

Jason Moore, PhD, chair of the Department of Computational Biomedicine and a professor of Medicine, said the study provides critical information and insights for future pandemics.

“The age-old debate of what matters most—genetics or your environment—continues through the work of this important study,” said Moore. 

Funding: This study was supported by the National Institutes of Health National Institute of General Medical Sciences R35GM131905.

Other authors involved in the study include Vijendra Ramlall, Michael Zietz, and Undina Gisladottir.

Read more from the Cedars-Sinai Blog: COVID-19 and Flu Shots Provide a Double Dose of Protection

Known as the eXtended-Reality Artificially Intelligent Ally, or XAIA, the program offers users an immersive therapy session led by a trained digital avatar. Findings from a first-of-its-kind study were published today and showed that participants benefited from the sessions.

“To our knowledge, this is the first time mental health therapy has been studied using generative artificial intelligence within immersive virtual reality,” said Brennan Spiegel, MD, MSHS, professor of Medicine, director of Health Services Research at Cedars-Sinai and first and corresponding author of the study published in the peer-reviewed journal Nature Digital Medicine.

Cedars-Sinai investigators created XAIA as a way to offer patients self-administered, AI-enabled, conversational therapy in relaxing virtual reality environments—such as a creek-side meadow or a sunny beach retreat where patients can also do deep breathing exercises and meditation.

As highlighted in the study, participants interacted with XAIA, which employs a large language model programmed to resemble a human therapist. The avatar was developed by Cedars-Sinai psychiatrist Omer Liran, MD, with input from Cedars-Sinai clinical psychologist Robert Chernoff, PhD, who co-authored the paper and helped assess the program for safety.

Expert mock therapy sessions were performed and transcribed to hear firsthand how a trained psychologist can—and should—interact with patients. From these sessions, recurring exchanges and patterns were identified and encoded into the large language model as rules and preferred responses for XAIA to emulate. These findings were then used to train the generative AI technology behind XAIA while incorporating over 70 best practices for mental health therapy.

“After teaching XAIA these skills, she offered therapy in a more empathetic, compassionate and meaningful way,” Chernoff said. “She handled a range of situations very well and did not show evidence of safety concerns during our testing.”

Fourteen participants with mild to moderate anxiety or depression interacted with XAIA for 30 minutes, then participated in debriefing interviews. Researchers then provided an analysis of the interview transcripts. Therapy topics ranged from loneliness and family problems to financial distress.

Participants frequently responded to the avatar as if it were a person and shared raw emotions. For example, one study participant told XAIA, “I feel like I’m a failure. The only thing I have to look forward to—I know you're going to laugh—is to go see Taylor Swift … and she's like the only thing that's keeping me alive. I'm sorry I'm crying.”

XAIA responded with comments like, “Firstly, there’s no need to apologize for crying. These feelings are valid.”

In another exchange, a participant told XAIA, “I think I look to other people to validate my self-worth and so rejection is especially painful. So, I am reluctant to put myself in situations where I could be rejected.”

XAIA responded, saying, “That's quite an insightful observation about yourself. It can be challenging when our self-worth is tied to the approval or validation of others. Fear of rejection can indeed make social interactions daunting. Could you share a little more about where you think this need for validation comes from?”

Participants described the program as easy to maneuver, and indicated that XAIA was “friendly,” “approachable,” “calming,” “empathic,” “empowering,” “unbiased” and “intelligent.” Participants also reported perceived advantages over traditional talk therapy, and all participants said they would recommend the program to others, although some indicated they would still prefer a human therapist if given the choice.

“These results provide initial evidence that VR and AI therapy has the potential to provide automated mental health support within immersive environments,” said Spiegel, director of the Cedars-Sinai master's degree program in health delivery science (MHDS) and the George and Dorothy Gourrich Chair in Digital Health Ethics. “By harnessing the potential of technology in an evidence-based and safe manner, we can build a more accessible mental healthcare system.”

When creating XAIA, investigators sought to help address a national shortage of psychotherapists, especially in rural areas where access to mental health services is often delayed or nonexistent.

“The prevalence of mental health disorders is rising, yet there is a shortage of psychotherapists and a shortage of access for lower income, rural communities,” Spiegel said. “While this technology is not intended to replace psychologists—but rather augment them—we created XAIA with access in mind, ensuring the technology can provide meaningful mental health support across communities.”  

Conflicts of Interest: Spiegel and Liran are co-founders of VRx Health. The XAIA technology is exclusively licensed from Cedars-Sinai to VRx Health for commercialization.

Funding: The study was conducted using internal resources from Cedars-Sinai with additional support from the Marc and Sheri Rapaport Fund for Digital Health Science and Precision Health at Cedars-Sinai.

Other Cedars-Sinai authors involved in the study include Omer Liran, MD, Allistair Clark, MA; Jamil S. Samaan, MD; Carine Khalil, PhD; Kavya Reddy, MD; and Muskaan Mehra, BS.

Follow Cedars-Sinai Academic Medicine on X for more on the latest basic science and clinical research from Cedars-Sinai.

Read more from the Cedars-Sinai Blog: Virtual Reality and the Brain-Body Connection

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“While there is a tremendous amount of excitement around artificial intelligence, particularly as it pertains to healthcare, there remains a gap between research findings on limited historical datasets and bringing unproven algorithms to the clinic and patients,” said Ouyang, a faculty member in the Department of Cardiology in the Smidt Heart Institute at Cedars-Sinai and in the Division of Artificial Intelligence in Medicine.

The recently launched journal will spotlight clinical trials and practice-changing innovations, along with reviews, policy perspectives, and educational materials designed specifically for practicing physicians and clinician leaders interested in leveraging AI.

“There’s a keen interest in understanding and showcasing the results of artificial intelligence in clinical practice and deployment, particularly with an eye on patient impact,” said Ouyang.

As a deputy editor, Ouyang is responsible for the curation, screening and review of many of the submitted studies and editorials about cardiology. He is the only cardiologist among the NEJM AI deputy editors.

At Cedars-Sinai, Ouyang’s research focus is on cardiac imaging as well as application of artificial intelligence and data science to healthcare. A statistician by training, Ouyang said he has always been fascinated by understanding, visualizing, and interpreting data. As a cardiologist, he is interested in the generation and assessment of clinical evidence and data.

“The Smidt Heart Institute and our broader academic enterprise have benefited greatly from David’s vast knowledge and expertise in large language models, artificial intelligence, imaging and cardiology,” said Christine M. Albert, MD, MPH, chair of the Department of Cardiology in the Smidt Heart Institute and the Lee and Harold Kapelovitz Distinguished Chair in Cardiology. “As an innovator in the field, we are particularly honored to have David represent Cedars-Sinai, personifying our commitment to lead in AI medical applications.”

Among his research achievements, Ouyang led the first-of-its-kind, blinded, randomized clinical trial of AI in cardiology. The study found AI was superior in assessing cardiac function in echocardiograms when compared with assessments made by sonographers. The findings were published in late 2023 in the peer-reviewed journal Nature.

Ouyang has published nearly 100 academic research studies in medical journals such as Journal of the American College of Cardiology (JACC), European Heart Journal, and Circulation.

Read more from the Cedars-Sinai Blog: Reducing Bias in AI Models

As Cedars-Sinai explores new uses of AI, it is balancing the rapid development of this emerging technology with responsible and ethical implementation.

“AI systems have the power to transform healthcare,” said Mike Thompson, vice president of Enterprise Data Intelligence at Cedars-Sinai. “If implemented properly and responsibly, AI can be deployed to enhance patient experience, improve population health, reduce costs and improve the work life of healthcare providers.” 

Thompson sat down with the Cedars-Sinai Newsroom to examine the uses of AI to improve healthcare and to detail how the academic medical center is pursuing this fast-evolving technology in an ethical manner.

Why do ethics matter when it comes to the use of AI in healthcare?

The integration of AI into medical technology and healthcare systems is only going to increase in the coming years. As technology continues to develop, the push toward safety, soundness and fairness occurs at all levels. This effort will require checks and balances from innovators, healthcare institutions and regulatory entities.

As technology advances, the medical community will need to develop standards for these innovative technologies, as well as revisit current regulatory systems on which physicians and patients rely to ensure that healthcare AI is responsible, evidence-based, bias-free, and designed and deployed to promote equity.

If AI systems are not examined for ethics and soundness, they may be biased, exacerbating existing imbalances in socioeconomic class, color, ethnicity, religion, gender, disability and sexual orientation.

Bias disproportionately affects disadvantaged individuals, who are more likely to be subjected to algorithmic output that are less accurate or underestimate the need for care. Thus, solutions for identifying and eliminating bias are critical for developing generalizable and fair AI technology.

Are AI ethics in healthcare different than AI ethics in other fields, like consumer goods?

While many general principles of AI ethics apply across industries, the healthcare sector has its own set of unique ethical considerations. This is due to the high stakes involved in patient care, the sensitive nature of health data, and the critical impact on individuals and public health.

It is critical that AI in healthcare benefit all sectors of the population, as AI could worsen existing inequalities if not carefully designed and implemented. It’s also critical that we ensure AI systems in healthcare are both accurate and reliable. Ethical concerns arise when AI is used for diagnosis or treatment without robust validation, as errors can lead to incorrect medical decisions.

What is an example of “AI ethics in action” at Cedars-Sinai?

As an example, consider an AI system that is used to assist in a patient’s risk for diagnosis. One question to ask is whether the AI algorithm performs equally for patients, regardless of race or gender.

In the same vein, an algorithm trained on hospital data from the European Union may not perform as well in the U.S., as the patient population is different, as are treatment strategies and medications.  

To combat these challenges, bias mitigation strategies may require us to implement mathematical approaches that help an AI model learn and produce balanced predictions.

At Cedars-Sinai, we also believe that critical AI algorithms should augment the expert, not replace that individual. Keeping the “human in the loop” to review a recommendation is another important strategy we use to mitigate bias.

Does Cedars-Sinai approach ethical AI differently than other academic medical centers? If so, how? And why?

To support our AI strategy, we created a framework for the ethical development and use of AI. The framework and policies are designed to ensure that the evolution of AI in medicine benefits patients, physicians and the healthcare community. It advocates for appropriate professional oversight for safe, effective and equitable use.

The framework starts by identifying who might be impacted and how, and then takes steps to mitigate any potential adverse impact.

How does the ethical use of AI evolve over time as Cedars-Sinai progresses in its use of these new technologies? 

The most powerful—and useful—AI systems are adaptive. These systems should be able to learn and evolve over time outside of human observation and independent of human control. This, however, presents a unique challenge in AI ethics, as it requires ongoing monitoring, review and auditability to ensure systems remain fair and sound.

Recent booms in AI technologies have been decades in the making. The most relevant and recent advances have accelerated the growth of AI algorithms and concepts—an evolution that will continue.

Now more than ever before, we must ensure that AI algorithms are trustworthy and deserving of trust. In healthcare, this entails systematically accumulating evidence, monitoring systems and data that are based on ethics and equity. 

Read more from the Cedars-Sinai Blog: The Human Factor of Artificial Intelligence

Through new “Idea-thons,” frontline clinical and administrative leaders and AI experts across the organization come together to cultivate ideas for using AI to enhance patient care, administrative systems and employee wellbeing. Participants then present their ideas to senior leaders, who vote on which projects to advance, support and, eventually, put into use across the organization.  

“Idea-thons empower our frontline caregivers and staff to shape the future of healthcare, tapping into their talent, creativity, and hands-on experience to craft tailored solutions for patients and the organization,” said Craig Kwiatkowski, PharmD, senior vice president and chief information officer at Cedars-Sinai. “We are leaning in to create our own road map of imaginative projects using generative AI technologies, centered on enhancing patient care and organizational efficiency.”

The Idea-thons are driven by Cedars-Sinai’s Artificial Intelligence Council, which is charged with guiding and coordinating the organization’s AI strategy. Cedars-Sinai’s AI strategy is built on three strategic pillars: investing and planning, transitioning innovation into adoption, and supporting the ethical, responsible, and scientifically sound use of AI, including generative AI.

Generative AI is a type of artificial intelligence that can create a series of outputs—like video, audio or texts—in response to human requests. These generative AI systems are based on language models like ChatGPT that can be trained to generate content.

The first Idea-thon, in June, brought together more than a dozen physicians from various medical specialties, including cardiology, emergency medicine, surgery, OB-GYN and primary care, as well as AI experts in data science, computational biomedicine and technology.

The session produced solutions with themes centered around care coordination, triage, medication management, communications and clinical trial recruitment. Some of the projects potentially overlapped with initiatives already underway at Cedars-Sinai. Others presented fresh opportunities to develop anew.

“These novel projects can be used alongside our customized generative AI tools and technologies within our health system,” said Mike Thompson, vice president of Enterprise Data Intelligence, who works with Kwiatkowski to steer the organization’s AI strategy.

Thompson said the Idea-thons are informed by the experience and wisdom of frontline healthcare clinicians and other Cedars-Sinai staff who understand firsthand the problems encountered in the delivery of healthcare.

“Our physicians and medical teams have direct insight into the roadblocks within their daily jobs,” Thompson said. “By bringing them into the discussion on generative AI technologies, they can help identify meaningful solutions to these hurdles—which will likely improve patient care, advance the frontiers of medical research and increase organizational efficiency.”

As these ideas transition into tangible solutions, Cedars-Sinai is dedicated to conducting rigorous testing and impact assessments to ensure alignment with the organization’s mission to deliver the highest-quality care. The collaborative teams involved in the Idea-thons aim to refine and tailor AI solutions to address the unique needs of both patients and the organization, while remaining steadfast to their strategic principles.   

The next Idea-thon, scheduled for October, will bring together an even broader group of healthcare professionals, including nurses, pharmacists and other clinicians. Another session will follow to include administrative and operational staff from Finance, Human Resources and other departments.

"We are truly inspired by the ingenuity of our staff and by the potential of the generative AI ideas we've heard,” Kwiatkowski said. “There are limitless opportunities for innovation within patient care, operational and administrative areas. We will continue listening to and seeking input from our frontline staff as we further explore how to best use these new tools.” 

Read more from the Cedars-Sinai Blog: Artificial Intelligence Advances

Building on a legacy of innovation, Cedars-Sinai is harnessing rapidly evolving breakthroughs in AI technology to enhance patient care, improve efficiency, advance scientific discovery, and cultivate greater physician and staff wellbeing.

AI at Cedars-Sinai already is having an early impact on clinical and research initiatives. Investigators, for example, are using the technology to identify the earliest signs of pancreatic cancer, predict sudden cardiac arrest and uncover genetic predictors of Alzheimer’s disease risk.

Cedars-Sinai leaders are excited by the possibilities afforded by AI—including the potential to reduce healthcare disparities and costs—as they guide the organization through this dynamic revolution in healthcare.

“AI extends and augments human capabilities and intelligence,” said Craig Kwiatkowski, PharmD, senior vice president and chief information officer. “It holds the potential to transform the ways we envision, plan and deliver care. Because of the vast opportunities, we are moving deliberately in these early stages of the journey.”

The Three Pillars of Artificial Intelligence at Cedars-Sinai

Cedars-Sinai’s AI strategy is built on three foundational strategic pillars: investing and planning, transitioning innovation into adoption, and supporting sound, ethical use of new technologies.

Through its first pillar, Cedars-Sinai is investing in state-of-the-art technology, infrastructure and services while fostering AI fluency across the workforce. The intent is to lay a foundation to meet the organization’s future healthcare needs.

The second pillar calls for the use of AI research and innovation to solve critical, real-world healthcare challenges—accelerating the integration of AI discoveries into clinical practice and delivering benefits to patients, physicians and the healthcare delivery system.

The third pillar focuses on the ethical and responsible use and governance of AI by adhering to regulatory requirements while ensuring that AI tools are used in fair and unbiased ways that protect patients and their privacy.

“The AI journey doesn’t have a finish line, and we must keep our eyes on the road ahead,” said Mike Thompson, vice president of Enterprise Data Intelligence, who works alongside Kwiatkowski to steer the organization’s AI strategy. “We are building a strong foundation for a future of limitless possibilities.”

Artificial Intelligence Council

An essential component of success involves the creation an Artificial Intelligence Council. It brings together cross-functional leaders—from patient care, research, data and technology teams—to review, guide and coordinate AI strategy. The council provides a forum for open dialogue and an ongoing exchange of ideas while setting priorities, evaluating the use of AI tools and identifying measures of success.

“The AI Council is a core piece of our commitment to using AI responsibly,” said Jason Moore, PhD, chair of the Department of Computational Biomedicine and a founding member of the council. “It is critical that we promote responsible AI principles, helping to ensure that AI is deployed safely, effectively and in an unbiased and transparent manner.”

Artificial Intelligence in Action

AI can accelerate scientific discovery by advancing the understanding of disease states and potential treatment pathways. At the same time, AI can improve the efficiency and accuracy of clinical data, freeing providers to spend greater face-to-face time with patients.

Early adoption of artificial intelligence already is making a difference in research and clinical programs at Cedars-Sinai. Examples include:

• Pancreatic Cancer: Cedars-Sinai investigators have leveraged AI to identify the earliest signs of pancreatic cancer, a disease notoriously difficult to diagnose in its early stages. By using advanced machine learning algorithms to analyze medical imaging scans and patient records, the AI system may help prevent deaths through early detection, leading to timely interventions and improved patient prognoses.
• Heart Health: Research led by investigators in the Smidt Heart Institute and the Division of Artificial Intelligence in Medicine in the Department of Medicine is helping clinicians get closer to predicting two common heart conditions: sudden cardiac arrest, which is often fatal, and increased coronary artery calcium, a marker of coronary artery disease that can lead to a heart attack.
• Brain Cell Modeling: Investigators from the Anastassiou Lab—members of the Departments of Neurology and Neurosurgery, the Board of Governors Regenerative Medicine Institute and the Center for Neural Science and Medicine at Cedars-Sinai—have created complex computer models of individual brain cells, unlocking new avenues for understanding brain function and neurological disorders.
• Alzheimer's Disease Research: An $8 million grant from the National Institutes of Health to study Alzheimer’s disease is enabling investigators to study new, leading-edge artificial intelligence methods and to use these to identify genetic predictors of Alzheimer’s disease risk.
• Liver Disease: Cedars-Sinai experts are investigating how ChatGPT may help improve outcomes for patients with cirrhosis and liver cancer by providing easy-to-understand information about lifestyle changes and treatments.
• Obstetrics and Gynecology: AI is helping physicians make headway in predicting the need for cesarean section delivery. By analyzing electronic health records, the Cedars-Sinai AI model can help physicians assess factors influencing the need for C-sections, potentially leading to better outcomes and informed decision-making for parents.
• Spine Surgery: The Department of Computational Biomedicine, in collaboration with Cedars-Sinai’s AI Council and spine surgeons, is using AI and machine learning to predict which patients are most likely to successfully manage their pain post-surgery and which ones might need additional assistance.
• COVID-19: During the height of the COVID-19 pandemic, Cedars-Sinai developed an AI model that helps physicians diagnose the severity of COVID-19 pneumonia. This technology already is being used in multicenter clinical studies.

Although AI initiatives are already having an impact at Cedars-Sinai, those leading the organization’s strategy are busily planning to expand the scope of applications.

“We are only at the very beginning of understanding what AI can do to improve healthcare and quality of life for our patients, our physicians and our staff,” Kwiatkowski said. “We are committed to building a firm foundation as we head into the future.”  

Read more in Discoveries: The Human Factor of Artificial Intelligence