The Time Is Now for Artificial Intelligence, Machine Learning

From artificial intelligence (AI) and data integration to natural language processing and statistics, the Cedars-Sinai Department of Computational Biomedicine is utilizing the latest technological advances to find solutions to some of the most complex healthcare issues.

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