“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.”

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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 model addresses a longstanding challenge in hospital care. Low blood sugar, also called hypoglycemia, is a common and potentially life-threatening complication among hospitalized patients, including those receiving diabetes treatment, those who are fasting before procedures or those in critical care. However, there are no widely used tools for predicting which hospitalized patients may develop hypoglycemia.

“Today, most hospital care for hypoglycemia is reactive, and we respond after a patient’s blood sugar drops,” said Roma Gianchandani, MD, senior author of the study and vice chair of Quality and Innovation in the Department of Medicine and program director for Diabetes.  

The AI model developed by Cedars-Sinai investigators analyzes patterns in medications, lab results, meals and other data from patients’ electronic health records. It collects the information in four-hour intervals over a five-day period and uses it to predict whether a patient will develop hypoglycemia within the next 24 hours.

Researchers developed and tested the model using data from more than 143,000 adult hospital admissions across three Cedars-Sinai Health System hospitals between 2014 and 2025. Investigators also tested the tool using prospective hospital data to confirm their initial findings.

“The AI model is designed to alert patient care teams before a patient experiences low blood sugar and identify the key factors driving that risk,” said Amanda Momenzadeh, PharmD, lead author of the study and a project scientist in the Meyer Research Lab at Cedars-Sinai. “By offering actionable insights to care teams, it also aims to support hospital diabetes management programs.”

Researchers estimate the tool could help prevent about three to four cases of low blood sugar at a large hospital each day. Extrapolating across all hospital beds worldwide, the impact could be substantial.

“What’s exciting is that this isn’t just a theoretical model, but instead, it is built and validated to work prospectively in real time using data hospitals already collect,” said senior author of the study Jesse Meyer, PhD, assistant professor in the Department of Computational Biomedicine at Cedars-Sinai. “By identifying patients at risk earlier, we have an opportunity to reduce preventable complications and improve patient safety.”

If widely adopted, the model could lead to more proactive, data-driven care for hospitalized patients with diabetes and other conditions that affect blood sugar.

Additional Cedars-Sinai authors: Caleb Cranney, Dennis Chen, and Elizabeth Nguyen.

Funding: NIGMS R35GM142502, NIH National Center for Advancing Translational Science (NCATS), and UCLA CTSI Grant Number UL1TR001881

Disclosure: Jesse Meyer, Amanda Momenzadeh, and Caleb Cranney are listed as inventors on a patent application related to this AI tool.

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.  

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But is agentic AI smart enough to replace actual scientists?

The Cedars-Sinai Newsroom sat down for a conversation with Jason Moore, PhD, chair of the Department of Computational Biomedicine at Cedars-Sinai, to tackle the pluses and minuses of agentic AI. Moore is corresponding author of a new paper, published in Nature Biotechnology, that examines where agentic AI is today and where it is headed.

What is agentic AI? Why is it called ‘in silico team science’?

Conducting biomedical research requires a team of specialists with expertise in different aspects of the medical issue being studied, from physiology to data collection and analysis, study design and writing. 

Agentic AI replicates this approach in a computer (in silico) by coordinating the activities of a “team” of AI solutions dedicated to completing specific tasks.

How does agentic AI help biomedical scientists?

As a researcher, I have a lot more ideas than I can actually pursue in my laboratory. Agentic AI is opening the door for me to explore more scientific questions than I otherwise could. It has allowed researchers in my lab to complete complex software-engineering and computer-programming projects in days rather than months, and we're seeing mind-boggling levels of productivity and efficiency.

These benefits are coming along at a particularly useful time. As the healthcare industry faces rising costs and reduced reimbursements, agentic AI can help labs be more efficient and survive with smaller teams.

Does this mean agentic AI will replace human scientists?

There's a lot of discussion about this in the AI community. I don't yet have confidence that AI can fully replace anybody in my research lab—and maybe it never will.

Humans do many things that AI may not be good at, such as managing people, displaying emotional empathy, and coming up with new hypotheses and creative solutions to problems. Those things are important if you want a research lab that functions well.

Trust is also a barrier. We know how to trust a human collaborator, but with potentially dozens of AI agents doing very complex things very rapidly, how do you know what they're doing? How can you be sure that what they've done is accurate?

From a broader perspective, how do we design ethical guardrails that ensure we put the human subjects of our scientific research first? And how do we find an environmentally sound way to provide the enormous amount of energy to fuel the computing power that AI requires?

Given these challenges, what do you view as the future of agentic AI?

The genie's out of the bottle. This technology is here, and it’s going to affect absolutely everything we do in our professional and personal lives. It is going to turn things upside down.

The exciting thing for biomedical science is that agentic AI will allow each person to be 10 or 20 or 100 times more efficient. And assigning tasks to AI allows us to focus more on the human skills, the creativity and the emotional side of what we do. It has the potential to accelerate scientific discoveries and the translation of those discoveries into better healthcare practices.

I try not to make specific predictions because all of this is moving so quickly and unpredictably. But I think the one prediction I can make about agentic AI is that everything's going to be different a year—or even six months—from now.

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More than 30% of clinical trials fail due to adverse drug reactions, and the up to $5,054,235.00 contract award by the Advanced Research Projects Agency for Health (ARPA-H) Computational ADME-Tox and Physiology Analysis for Safer Therapeutics (CATALYST) program, will address this longstanding challenge in drug development.

“Each year, many promising drugs fail in trials because animal tests and short-term lab studies cannot predict how medicines behave in real people over time,” said Nicholas Tatonetti, PhD, vice chair of Computational Biomedicine at Cedars-Sinai and the project's lead investigator. “These failures delay lifesaving treatments and drive up drug development costs.”

The new platform, called KronosRx, aims to reduce these failures by applying AI tools to “patient avatars”—sophisticated organoids and organ-on-chip systems derived from human stem cells—to help investigators predict drug toxicity that might otherwise harm clinical trial participants.

The avatars use tiny numbers of cells to mimic the function of whole organs and their immediate response to experimental medications. The AI models in the platform are trained using millions of anonymous patient data points from Cedars-Sinai’s extensive electronic health record network. The resulting platform can forecast an organ’s response to a medication over time—and across the diverse population of patients reflected in the Cedars-Sinai data.

“These AI systems don’t just predict whether a drug is safe or toxic; they model how risk evolves dynamically, accounting for age, a patient’s health, and other medications they might be taking,” Tatonetti said.

Investigators hope this approach will allow better predictive modeling that can evolve over time, reducing reliance on animal studies and improving safety for all patients.

“By creating a more reliable and human-relevant method for safety assessment, the KronosRx project aims to improve clinical trials and to shorten development timelines,” said Clive Svendsen, PhD, executive director of the Cedars-Sinai Board of Governors Regenerative Medicine Institute and an investigator on the KronosRx project.

The Cedars-Sinai KronosRx team includes leaders in computational biomedical innovation, stem cell biology and health informatics.

Tatonetti is leading project integration using biomedical data science and AI-driven drug discovery methods. Svendsen is applying induced pluripotent stem cells and organ chip technologies to better understand how common drugs may cause rare neurological side effects.

Arun Sharma, PhD, director of the Cedars-Sinai Center for Space Medicine Research in the Board of Governors Regenerative Medicine Institute, is using patient-specific cardiac organoid and organ chip systems to assess drug-induced cardiotoxicity. Graciela Gonzalez-Hernandez, PhD, professor and vice chair for Research and Education in the Department of Computational Biomedicine, is advancing the project’s AI and unstructured text data integration to connect molecular and clinical phenotypes.

The ultimate goal, Svendsen said, is to make critical treatments available to patients sooner.

“This approach allows AI to continually refine its forecasts as new evidence emerges, bridging the gap between computational prediction and real-world patient outcomes,” Svendsen 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.

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

Accreditation was granted in May 2025, less than six months after the PhD in Health AI program was submitted for review. It 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.

“Accreditation is a meaningful milestone for the PhD in Health AI program,” said Graciela Gonzalez-Hernandez, PhD, director of the program and professor and vice chair for Research and Education in the Department of Computational Biomedicine. “It signifies that the highest standards of academic excellence and innovation are at the heart of our curriculum, and it signals to prospective students, as well as our faculty and partners, that we’re pioneering a new kind of doctoral training with quality and rigor.”

In their report, commission evaluators praised the program for integrating doctoral-level learning into real-world work experiences and for offering extensive academic resources and support services for students’ coursework and research.

“Our Health Sciences University continues to evolve, with our newest PhD program enabling us to train the next generation of AI experts specifically focused on healthcare,” said Jeffrey A. 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. “As AI is poised to rapidly transform medicine, accreditation for the PhD in Health AI program helps further reflect and reinforce our commitment to shaping the future of healthcare.”

The PhD in Health AI program aims to attract applicants from diverse fields beyond healthcare, including computer science, engineering, math and gaming. Through an active-learning and structured mentoring model, students will gain exposure to real-world clinical environments and collaborate closely with clinicians and scientific investigators.

In addition to laboratory rotations with Cedars-Sinai’s AI research faculty, the program also includes clinical rotations—rare for nonmedical PhD students—to help participants understand how clinical information is generated and used.

“We look forward to welcoming our first cohort of exceptional students this week—each with a strong technical background and a shared commitment to improving healthcare,” Gonzalez-Hernandez said. “Throughout their time at Cedars-Sinai Health Sciences University they will engage with faculty, clinicians and each other to meaningfully and ethically apply AI in real clinical settings. They will graduate with a deep understanding of real-world healthcare challenges, positioning them for successful careers in academic research, industry, healthcare innovation, and public policy.”

Cedars-Sinai’s Health Sciences University was established in 2024. The university offers other graduate degrees, including a PhD in Biomedical Sciences, Master of Science in Health Systems and 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 career levels.

Read more in Cedars-Sinai Discoveries Magazine: Next Generation Health Education

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

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

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

Both studies used an application, developed at Cedars-Sinai, that combines AI and virtual reality (VR) goggles. It features avatars that are “trained” by AI to conduct talk therapy with patients in virtual and relaxing environments.

In the first study, published in the Journal of Medical Extended Reality, investigators used the VR application to deliver mental health therapy to 20 patients with alcohol-associated cirrhosis, a serious liver disease that can result from long-term excessive alcohol consumption.

Each patient received a 30-minute counseling session from a virtual therapist avatar that had received AI training in motivational interviewing, cognitive behavioral therapy and other techniques to help patients alter their behaviors. More than 85% of the patients said they found the sessions beneficial and 90% expressed interest in using virtual therapists again.

“For individuals awaiting liver transplants for cirrhosis, alcohol addiction remains a high-risk factor,” said Brennan Spiegel, MD, MSHS, professor of Medicine, director of Health Services Research at Cedars-Sinai and the corresponding author for both studies. “We see VR as a way to augment traditional interventions, which often fall short due to a shortage of mental health professionals, societal stigmatizing of alcoholism and other factors.” Spiegel also participated in recent research demonstrating that VR experiences can even modulate stress levels and immune responses in participants.

In the second study, published in the journal Cyberpsychology, Behavior, and Social Networking, investigators presented virtual therapists with virtual patients that had been trained by AI to emulate people seeking professional help for anxiety or depression. In each simulated conversation, the virtual therapist was randomly informed of a different sociodemographic profile of the patient based on age, gender, race, ethnicity and annual income. A control group without assigned identities was also included.

The investigators used a standard scale known as “tone analytics” to rate the tone or mood of the language used by the virtual therapist. In repeated samplings involving more than 400 conversations, no significant difference was found in the therapist’s tone score based on a virtual patient’s profile or the absence of a profile. “This data suggests that with thoughtful design, AI can offer equitable and personalized care,” Spiegel said.

These studies highlight the leading role Cedars-Sinai is taking to adopt new technologies and to develop patient-centered approaches to health challenges, said  Peter Chen, MD, professor of Medicine, the Medallion Chair in Molecular Medicine and interim chair of the Cedars-Sinai Department of Medicine.

“These two studies underscore Cedars-Sinai’s commitment to exploring the tremendous potential of artificial intelligence for mental health therapy while ensuring that this technology does not perpetuate human biases in delivering healthcare,” Chen said.  “Cedars-Sinai has become a world leader in tackling this formidable challenge.”

Other Cedars-Sinai authors of the Journal of Medical Extended Reality study include Yee Hui Yeo, Allistair Clark, Muskaan Mehra, Itai Danovitch, Ju Dong Yang, Alexander Kuo, Hyun-Seok Kim, Aarshi Vipani, Yun Wang, Walid Ayoub, Hirsh Trivedi, Jamil S. Samaan and Omer Liran. Additional authors were Karen Osilla, Tiffany Wu, and Vijay H. Shah.

Disclosures: Spiegel and Liran are faculty members at Cedars-Sinai Medical Center and co-founders of VRx Health, Inc. Spiegel has no role in the company, has received no payments, royalties, or proceeds from the company, has fully divested all equity and interests in VRx Health, Inc., and has divested from any interest and any potential royalties. Liran is on the Board of VRx Health and maintains equity; he is also eligible to receive future royalties. The AI algorithm in this study was exclusively licensed by Cedars-Sinai to VRx Health for commercialization. Cedars-Sinai has the right to receive future royalty payments from VRx Health under this license agreement and owns stock in VRx Health. All other authors do not have conflict of interest.

Other Cedars-Sinai authors of the Cyberpsychology, Behavior, and Social Networking study include Yee Hui Yeo, Muskaan Mehra, Jamil S. Samaan, Joshua Hakimian, Allistair Clark, Karisma Suchak, Zoe Krut, Taiga Andersson and Omer Liran. Additional authors were Yuxin Peng and Susan Persky.

Disclosures: Spiegel and Liran are faculty members at Cedars-Sinai Medical Center and co-founders of VRx Health, Inc. The AI algorithm in this study was exclusively licensed by Cedars-Sinai to VRx Health for commercialization. Cedars-Sinai has the right to receive future royalty payments from VRx Health under this license agreement, and owns stock in VRx Health. All other authors do not have conflict of interest.

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

Their achievement, described in the peer-reviewed journal Nature Communications, significantly expands AI’s healthcare applications. 

The new AI method, also called a model, offers a much-needed alternative to tumor registries, the databases maintained by governments and hospitals. Researchers normally use tumor registries to screen cancer patients for clinical trials. Cancer registries require specially trained employees to manually identify a patient’s cancer stage by reviewing laboratory reports, clinicians’ notes and other information. The process can be slow and tedious.

“By the time a cancer patient’s data is entered into a tumor registry, months may have passed, along with the opportunity for the patient to participate in relevant clinical trials or other treatments,” said Nicholas Tatonetti, PhD, vice chair of Computational Biomedicine at Cedars-Sinai, associate director for Computational Oncology at Cedars-Sinai Cancer and corresponding author of the study. “Our AI model can dramatically reduce that delay, accelerating the pace of research and expanding patients’ access to clinical trials.”

The team’s AI model quickly identifies the cancer stage by extracting and interpreting the text of just one element of a patient’s electronic health record: the pathology report, which describes the findings of pathologists examining tissue specimens from the patient. In tests involving thousands of patient records, the study’s investigators confirmed that the AI model they created was highly effective in staging patients’ cancers.

The method is based on a so-called transformer model of AI, which is designed to simulate the complex decision-making power of the human brain. The study team first “trained” the model to stage cancers using publicly available pathology reports from a government database, The Cancer Genome Atlas. These reports covered nearly 7,000 patients and included 23 types of cancers.

To make sure the model worked in various settings, investigators then applied it to nearly 8,000 pathology reports maintained by a single medical center. The results, as measured by a standard statistic for evaluating AI models, rated the method as highly accurate. “This was an important finding because it means that our AI model is an ‘off- the-shelf’ tool that can be generalized to other institutions without requiring that it be trained for each location,” Tatonetti said.

Besides screening patients by their cancer stages for clinical trials, the AI model also can be used to automate classification of patients for observational and retrospective data analysis, and for potential treatments, according to Tatonetti. “Future research could build on our method to integrate the pathology text with other types of clinical data, potentially advancing personalized cancer treatment,” he said.

The creation of the AI model was made possible by earlier research, also led by Tatonetti, that removed technical obstacles to computers extracting and analyzing pathologists’ notes from electronic health records.

In a notable decision, the investigators in the new study have made their AI model, which they named BB-TEN: Big Bird – TNM staging Extracted from Notes, available to other institutions for academic uses and certain other purposes.

“By speeding up the selection of candidates for cancer clinical trials, this innovative AI model shows promise for accelerating the development of relevant treatments and making them available to more patients,” said Jason Moore, PhD, chair of the Department of Computational Biomedicine at Cedars-Sinai.

Other Cedars-Sinai authors include Jacob Berkowitz, Jose M. Acitores Cortina and Kevin K. Tsang. An additional author was Jenna Kefeli.

Kefeli and Tatonetti were supported by award number R35GM131905 from the National Institute of General Medical Sciences of the National Institutes of Health.

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

A recent opinion column published in the Cell Press journal Patterns lays out what the investigators see as the challenges and opportunities for those who identify as lesbian, gay, bisexual, transgender and queer or questioning (LGBTQ+) to increase their representation in these fields. The column emphasizes the importance of addressing the biases and erasure of gender and sexual diversity in data and computational models.

“Our team works to improve patients’ lives by using computers, computing technologies and data resources; insights from a diverse community are invaluable in helping us do that,” said Jason Moore, PhD, professor and chair of the Department of Computational Biomedicine and an investigator who helped author the opinion article.

Moore said it’s not only a matter of tolerance and acceptance: “LGBTQ+ inclusion is necessary to enhance scientific problem-solving, generate more equitable knowledge and address the health disparities and needs of diverse communities. Not doing so can impact computer models and all patients, potentially leading to harmful conclusions.”

The column’s authors propose strategies and resources for LGBTQ+ inclusion, such as improving data collection and model evaluation, revising policies, creating gender- and sexual diversity-inclusive curricula, and fostering allyship and support networks. They provide examples of conferences, organizations and programs that promote LGBTQ+ participation and leadership in STEM and AI, and they acknowledge the contributions of LGBTQ+ scientists in the history and development of STEM and AI.

To learn more about the team’s perspectives, the Cedars-Sinai Newsroom recently talked with two of the paper’s authors—Pei-Chen Peng, PhD, an assistant professor in the Department of Computational Biomedicine, and Ryan Urbanowicz, PhD, a research assistant professor and director of Cedars-Sinai’s National AI Campus, a U.S.-wide AI and machine learning collaborative and project-based initiative. 

Why was it important to bring this topic to the forefront?

Urbanowicz: Considering the ever-increasing use of AI in clinical care and research, it seemed timely to encourage open discussions about the unique challenges of the LGBTQ+ community and how they overlap with STEM and AI, both from the perspective of encouraging and supporting LGBTQ+ scientific trainees, and in how to tackle the challenges and considerations of LGBTQ+ in STEM and AI research.

Peng: When the Patterns journal invited us to submit an opinion article related to the experiences of queer scientists for its June issue, we saw this as a great opportunity to further highlight the experiences of the LGBTQ+ community and to advocate for queer scientists. As we wrote in the article, LGBTQ+ scientists are underrepresented in the biomedical AI and STEM community, which can cost the scientific community great minds and stifle innovation.

Why is it critical to have LGBTQ+ inclusion in STEM education?

Peng: The awareness of gender and sex diversity in STEM education inevitably influences clinical research methods. Inclusion of LGBTQ+ ensures that the STEM community is reflective of the broader society and reduces biases when we do biomedical and AI research.

Urbanowicz: I think that fostering a diverse community of STEM trainees ensures a more diverse set of perspectives and ideas, essential to our research community’s ability to do its best work as a whole.

Is there an area of opportunity that you are particularly passionate about?

Urbanowicz: Personally, I want to do my part as an LGBTQ+ scientist to be a visible and accessible example/role model to other LGBTQ+ trainees in STEM and AI research. Also, I want to bring awareness to the current limitations of AI, specifically with regard to potential disparity.

Peng: I’m excited about the potential for positive changes. We’ve highlighted the importance of inclusivity and representation, addressing how diverse perspectives can lead to more innovative and equitable scientific advancements, and we encourage all scientists to take initiative in advancing our understanding of LGBTQ+ health issues. I’m also proud that we were able to provide a catalog of existing educational and professional resources that the community can refer to as needed. For example, trainees and researchers will know which conferences are inclusive and promote diversity, and they can choose venues where they can present their research findings confidently.

What are some ways that AI models can ensure health equity for LGBTQ+ people?

Urbanowicz: When it comes to both medical and AI research, awareness of what makes the LGBTQ+ community unique and taking these considerations into account when it comes to data collection, study design and research resources should all improve overall health equity.

Based on the opportunities identified in your article, how can Cedars-Sinai be more inclusive?

Peng: Our National AI Campus is one area that comes to mind. It’s a training program that aims to make AI accessible to a diverse community with training experiences that focus on biomedical projects. Beyond promoting participants’ diversity, the program also could address LGBTQ+ issues by developing or inviting AI and/or machine learning projects that tackle LGBTQ+ health and analytical challenges.

Urbanowicz: I agree with Dr. Peng. National AI Campus is founded on principles of inclusion and accessibility for all students interested in learning about machine learning and artificial intelligence. In the future, in addition to introducing projects that tackle LGBTQ+ issues, we can encourage more LGBTQ+ participants to get involved and highlight the importance of these issues to trainees at large.

Read more on the Cedars-Sinai Blog: A Place at the Table to Shape Cancer Research

In a special conversation to mark Women’s Health Month, maternal-fetal medicine specialist Melissa Wong, MD, spoke with the Cedars-Sinai Newsroom about specific AI applications developed at Cedars-Sinai and being used to improve maternal health outcomes and gynecological cancer screening. Wong is also director of Informatics and Artificial Intelligence Strategies for the Department of Obstetrics and Gynecology.

What are some significant results of using AI to improve pregnancy and childbirth outcomes?

Two important studies looked at the leading causes of death during pregnancy and childbirth: preeclampsia and postpartum hemorrhage.

We know that low-dose aspirin can reduce the risk of developing preeclampsia, a dangerous hypertensive complication of pregnancy that can cause serious illness or death. Black pregnant women are particularly vulnerable to being overlooked for aspirin treatment. Our study found that using artificial intelligence to identify patients at risk for preeclampsia—and then automate the decision-making about prescribing the aspirin—led to an increase in appropriate aspirin treatment and also eliminated the racial disparity in care.

In another study using an application of AI, machine learning, we developed an algorithm that can help predict which patients are at an increased risk for severe complications from bleeding after childbirth. The key was to analyze data at many different points of a patient’s labor and delivery—from underlying medical conditions to the kind of anesthesia the mother received. Our next step is to see if the algorithm can help predict hemorrhages in real time and allow for intervention and saving lives.

What future uses of AI for OB-GYN care are being considered?

In the area of gynecological care, we are wrapping up a study that could improve the evaluation of the Pap smear used to check for cervical cancer. We do hundreds of Paps a week in our health system. The results are most meaningful when you can put them in the context of the patient’s history. We’ve developed an AI application using ChatGPT that performs a contextual analysis of results, produces a recommendation for next steps, if needed, and even generates a letter to the patient about the results.

Gestational diabetes and hypertension during pregnancy are two conditions we believe could also benefit from AI interventions to identify trends and patterns in patient-submitted data and recommend interventions.

In the case of diabetes, about 80% of the patients submitting data from their glucose monitors are probably doing well or just need straightforward adjustments to their insulin. But our expert diabetes educator still has to analyze all of the data, even if only 20% of the patients will require direct intervention with medication or nutrition counseling to get them back on track. AI could help us focus on the smaller group of patients who need more complex and nuanced support, more expeditiously.

As for managing high blood pressure during pregnancy, this is often a new skill patients learn in pregnancy or postpartum. It’s also a complicated one. Trying to distinguish between a somewhat worrisome reading and one where you need to call your provider asap is not intuitive. A good AI model could send an alert when subtle pattern changes signal a need for medication modifications or urgent attention by the patient and provider.

What is the approach to AI innovation at Cedars-Sinai?

I feel incredibly fortunate to be here, not just because we are at the forefront of investigating the use of artificial intelligence across a wide variety of specialties and procedures, but because we do it with implementation in mind. There is a lot of AI research in the U.S. but not much that goes beyond the lab. At Cedars-Sinai, the goal is to research the myriad possible applications, and then when we’ve refined and tested them, to “turn it on,” so to speak. We are highly motivated to implement what we have developed and to help improve the delivery of care and the health of our patients.

What is the primary goal of harnessing AI for healthcare?

It’s about how AI can help healthcare providers free up their brains and their time to focus on delivering the best possible care. It can be a remarkable tool that brings us front and center with the patient again and moves us away from the kind of work that AI and machine learning do more effectively, quickly and accurately.

Whether a provider is in a healthcare desert with limited resources or at an academic medical center, the best applications of artificial intelligence will have a democratizing impact—reducing healthcare inequalities and providing care that could be more personalized across a broad spectrum of patient populations.

Read more from the Cedars-Sinai Blog: How Women Can Strengthen Their Health at Any Age

and their Cedars-Sinai colleagues can detect a type of abnormal heart rhythm that can go unnoticed during medical appointments, according to a new study.

The study’s findings, published in npj Digital Medicine, suggest AI could one day be employed to analyze images from a common imaging test called an echocardiogram, which uses sound waves to capture pictures of the heart.

Abnormal heart rhythms are often caused by—and also lead to—heart structure abnormalities. Researchers hypothesized that an AI program trained to analyze echocardiograms might help clinicians detect early, subtle changes in the hearts of patients with undiagnosed arrhythmias.

“We were able to show that a deep learning algorithm we developed could be applied to echocardiograms to identify patients with a hidden abnormal heart rhythm disorder called atrial fibrillation,” said Neal Yuan, MD, a staff scientist with the Smidt Heart Institute and first and corresponding author of the study. “Atrial fibrillation can come and go, so it might not be present at a doctor’s appointment. This AI algorithm identifies patients who might have atrial fibrillation even when it is not present during their echocardiogram study.”

During atrial fibrillation, the heart's upper chambers sometimes beat in sync with the lower chamber and sometimes they do not, making the arrhythmia often difficult for clinicians to detect. In some people, atrial fibrillation causes no symptoms. In others, it can cause heart palpitations, fatigue, shortness of breath, dizziness, and other symptoms that interfere with daily life. Left untreated, atrial fibrillation can cause stroke and heart failure.

An estimated 12.1 million people in the United States will have atrial fibrillation in 2030, according to the Centers for Disease Control and Prevention (CDC). Deaths related to atrial fibrillation have been increasing for more than two decades, according to CDC data.

“We’re encouraged that this technology might pick up a dangerous condition that the human eye would not while looking at echocardiograms,” said David Ouyang, MD, a cardiologist in the Department of Cardiology in the Smidt Heart Institute and a researcher in the Division of Artificial Intelligence in Medicine, and a senior author of the study. “It might be used for patients at risk for atrial fibrillation or who are experiencing symptoms associated with the condition.”

The team trained a program to study more than 100,000 echocardiogram videos from patients with atrial fibrillation. The program distinguished between echocardiograms showing a heart in sinus rhythm (a period of normal heart beating) and echocardiograms showing a heart in an irregular heart rhythm. The program predicted which patients in sinus rhythm had experienced or would develop atrial fibrillation within 90 days.

The model evaluating the images performed better than estimating risk based on known risk factors.

“The fact that this program predicted which patients had active or hidden atrial fibrillation could have immense clinical applications,” said Christine M. Albert, MD, MPH, chair of the Department of Cardiology in the Smidt Heart Institute and a study author. Being able to identify patients with hidden atrial fibrillation could allow us to treat them before they experience a serious cardiovascular event.”

Other Cedars-Sinai investigators who worked on the study include Nathan R. Stein, MD; Grant Duffy; Roopinder K. Sandhu, MD; Sumeet S. Chugh, MD; Peng-Sheng Chen, MD; Carine Rosenberg,^, Susan Cheng, MD, and Robert J. Siegel, MD.

This work was funded in part by the National Institutes of Health (K99 HL157421, R01HL139829), grants OT2OD028190, AHA 23IPA1052289, the Burns & Allen Chair in Cardiology Research, and Cedars-Sinai.

Read more from the Cedars-Sinai Blog: Atrial Fibrillation—Know the Warning Signs

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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The investigators, whose results are published in the peer-reviewed journal npj Digital Medicine, say their AI measurement tool offers new help in predicting heart attacks and cardiovascular disease.

“Our research shows that people with a larger volume of heart fat—a key indicator of metabolic health—and those with more dense heart fat—a marker of inflammation—are at higher risk for cardiovascular disease,” said Piotr J. Slomka, PhD, director of Innovation in Imaging at Cedars-Sinai and a professor of Medicine in the Division of Artificial Intelligence in Medicine. “This novel technology uses AI to more quickly and accurately assess the volume and density of heart fat, offering valuable insight beyond traditional methods.”

Slomka and colleagues hope to translate these findings into clinical use more broadly, offering doctors and clinicians this specialized software.

“These results underscore the efficiency and clinical importance of AI in heart disease risk assessment, offering a fast and reliable tool for predicting cardiovascular risk,” said Slomka, also a professor of Cardiology in the Department of Cardiology in the Smidt Heart Institute at Cedars-Sinai.

Investigators included 8,781 patients from four clinical sites in their study. None of the patients had known coronary artery disease—a type of heart disease—at the time of the study, and all underwent heart imaging.

Key results include:

• AI measurements of heart fat were performed in under two seconds, compared with 15 minutes typically required for manual measurement.
• During the follow-up period, which was 2.7 years, individuals with larger volumes of heart fat and higher densities of heart fat experienced greater incidence of heart-related issues or death.
• The risk of cardiovascular-related death, and heart attacks, was almost three times higher in patients with both elevated heart fat volumes and density.
• The association with cardiovascular risk persisted, even after taking into account other risk factors like age, medical history, past heart imaging results and coronary artery calcium scores.

Study results align with previous research but offer new insight through the use of AI.

“These findings validate the use of AI for quick and accurate heart fat measurement, highlighting a potential shift toward more AI-assisted diagnostic methods in cardiology,” said Sumeet Chugh, MD, Cedars-Sinai’s director of the Division of Artificial Intelligence in Medicine and associate director of the Smidt Heart Institute.

Chugh, who was not involved in the study, said that by establishing a clear link between heart fat and cardiovascular risk, the study encourages further research into heart fat as a key factor in heart disease.

“This approach serves as a practical, real-world test of the application's effectiveness when implemented in clinical settings,” Chugh said.

As a next step, investigators plan to further test their algorithm in a more diverse patient population to ensure accuracy and generalizability of the findings.

Other authors involved in the study include Robert J. H. Miller, Aakash Shanbhag, Aditya Killekar, Mark Lemley, Bryan Bednarski, Serge D. Van Kriekinge, Paul B. Kavanagh, Joanna X. Liang, Valerie Builoff, Attila Feher, Edward J. Miller, Andrew J. Einstein, Terrence D. Ruddy, Daniel S. Berman, and Damini Dey.

This research was supported in part by grants R01HL089765 and R35HL161195 from the National Heart, Lung, and Blood Institute at the National Institutes of Health (PI: Piotr Slomka).

Read more on the Cedars-Sinai Blog: Graduate Students Are Using AI in Medical Imaging

Imagine a world in which the digital watch on your wrist tracks not only your step count, but also your blood sugar, heart rate, blood pressure and respiration. Then, the watch automatically sends a personalized health snapshot to your physician, alerting them to early signs of disease.

That scenario could become reality in the near future, according to Joseph Schwab, MD, director of the Cedars-Sinai Center for Surgical Innovation and Engineering, who is leading innovative research in wearable healthcare technologies.

Schwab, also Cedars-Sinai’s director of Spine Oncology for Orthopaedic Surgery, will present the latest trends in his research about wearable health technology during the American Academy of Orthopaedic Surgeons (AAOS) Annual Meeting in San Francisco, Feb. 12-16. During the conference, Schwab also will participate in the President’s Symposium, sharing insights on generative artificial intelligence (AI), such as ChatGPT. He also will discuss healthcare privacy issues that can occur with these technologies.

The Cedars-Sinai Newsroom sat down with Schwab to discuss the scope of his research and how he sees AI impacting the future of healthcare.

What makes your lab different from other research facilities?

My co-director, Hamid Ghaednia, PhD, is a mechanical engineer and our lab is unique in the sense that it is heavily engineering-based. A lot of what we are doing day to day is building things. So rather than test tubes and microscopes, we have lathes and band saws. We have several 3D printers and a whole room dedicated to electronics, where we solder devices together. The research team’s engineering expertise is a key differentiator, and our clinical and engineering partnership is distinctive to what we do. Not only do we have the equipment, but we have the know-how to go with it.

We are one of the few research facilities in the country where we can identify a clinical need, discuss it, come up with a potential solution, build that solution and begin testing, all within one center.

What are a few of the innovations you’re working on?

Our focus area is wearable devices. Consumer wearables on the market are essentially motion trackers. They may have an accelerometer or gyroscope that can simply measure your position or motion to track steps and other data. What we’re doing is different in that our devices are sending energy—in the form of light, electrical energy and sound—into the tissues, and we can measure that energy as it leaves the tissue, and we can deduce things based on how the energy was affected by the tissue.

For example, when you are at the doctor and they use a reflex hammer above your knee to test for a reflex reaction, they are only able to identify the presence or absence of the reflex. Instead, wearable devices that we are developing can quantitate the reflex response—things like how long it took to respond, the robustness of the response, etc. We can give a very specific number connotation to these data points, which we hope will translate into better diagnoses.

How is AI used in the work you do?

The sensors on our wearable devices receive an incredible amount of data from the energy after it has traveled through the tissue, which requires advanced computing power to interpret. At its core, AI is just that—very advanced mathematics and computer programming. We utilize AI to interpret the data captured and correlate it to clinical problems.

Separate from our wearable technologies, we are also able to use AI to make predictions on a smaller scale for use in clinical practice, such as interpreting electronic health data. For instance, a patient may be considering a procedure that has a 5% risk of complication for the whole population; however, using AI to interpret their personal health information, we may learn that their individual complication risk is closer to 25%. This could heavily impact their decision-making. Giving more precise predictions like this is a form of personalized medicine.

Who can benefit from these new technologies?

These technologies can truly benefit everyone across the healthcare spectrum. Patients whose health data is analyzed could receive more personalized care. They could be directed to more precise tests to get an accurate diagnosis and personalized treatments, for example, and may ultimately have better outcomes.

There’s even the potential for a positive impact on healthcare payers and insurance companies by making the right treatment decisions, and thus reducing health expenditures. There are so many opportunities and benefits.  

Where do you see this field headed in the next five to 10 years?

In my opinion, it won’t be too long before we stop using the terms artificial intelligence and machine learning altogether because it will just be integrated into everything we do, running in the background as common practice. It will no longer be a mystery.

As it relates to wearable technologies, I see these becoming part of the expected process of medical assessments. There is the opportunity to learn so much more through these devices than you would glean from a basic physical examination, and the data can be captured in advance of a patient even seeing the provider. A medical appointment can become much more accurate and efficient when the provider has already been able to review and interpret the data collected.

Wearable technology and the integration of AI into the consumption and delivery of healthcare is only going to continue to grow with time, and I think people will become very comfortable and begin to rely on these devices in a good way.

Read more from Cedars-Sinai Discoveries Magazine: Artificial Intelligence Advances