During this session participants will learn about the impact that artificial intelligence (AI) and machine learning (ML) will have on the practice of medicine, and subsequently medical education. Participants will learn what AI and ML are, and current applications in healthcare. Finally, participants will be able to identify opportunities for incorporating AI/ML content into their curricula.
Presenter Bios
Dr. James is a Clinical Assistant Professor in the Departments of Internal Medicine, Pediatrics and
Learning Health Sciences at the University of Michigan (U-M). He is a primary care physician, practicing as a general internist and a general pediatrician.
Dr. James has served in many educational roles across the continuum of medical education. He also serves on local and national medical education committees. In multiple years Dr. James has been identified as one of the top teachers in the Department of Internal Medicine. In addition, in 2022 he received the pre-clinical Kaiser Permanente Excellence in Teaching award, the most prestigious teaching award given by the U-M medical school.
Dr. James has completed the American Medical Association (AMA) Health Systems Science Scholars program, and he was also one of ten inaugural 2021 National Academy of Medicine (NAM) Scholars in Diagnostic Excellence. As a NAM scholar, he began working on the Data Augmented, Technology Assisted Medical Decision Making (DATA-MD) curriculum. The DATA-MD curriculum is designed to teach healthcare professionals to use artificial intelligence (AI) and machine learning (ML) in their diagnostic decision making. Dr. James is also leading the DATA-MD team as they develop a web-based AI/ML curriculum for the AMA.
He has published articles in JAMA, Annals of Internal Medicine, Academic Medicine, the Journal of General Internal Medicine, Cell Reports, and more.
He is interested in curriculum development, and teaching learners to provide evidence-based, data-driven, equitable, patient-centered care. His research interests include clinical reasoning, implementation of AI/ML curricula across the continuum of medical education, and implementation of digital tools into clinical practice.
intersection of artificial intelligence (AI) and medicine, with specific research interests spanning clinical informatics, machine learning, and operations research. Erkin is a seventh-year Medical Scientist Training Program Fellow (MD-PhD student) at the University of Michigan. His doctoral research focused on creating AI tools for patients, physicians, and health systems. He has led work across the AI lifecycle with projects advancing from development to validation, technical integration, and workflow implementation. Erkin was co-advised by Dr. Brian Denton (Industrial and Operations Engineering) and Dr. Jenna Wiens (Computer Science and Engineering). Additionally, Erkin is interested in incorporating education about AI tools into undergraduate medical curricula. He has a professional background in health IT development, having worked at Epic, and holds a Master’s of Engineering from the University of Wisconsin. After completion of his MD-PhD training, he plans on pursuing residency training.