Instructor: Cornelius A. James, MD

Location: College Building Room 201, Center City Campus

Artificial Intelligence (AI) is transforming the practice of medicine. As AI algorithms become ubiquitous in health care, training clinicians to effectively engage with these technologies is essential. During this talk Dr. James will provide a vision for what AI may look like in medical education curricula, describe curricula integrated at the University of Michigan, and give an overview of the DATA-MD team’s efforts to provide AI-related educational resources to clinicians and organizations around the world. 

By the of this session participants should be able to: 

1. Recognize the role that AI/ML will play in clinical practice and medical     education.  

2. Describe the current state of AI/ML in medical education. 

3. Identify opportunities for incorporation of AI/ML into medical education curricula. 

4. Summarize current initiatives designed to teach clinicians to use AI/ML in clinical practice. 

*Dr. James is an Assistant Professor in the Departments of Internal Medicine, Pediatrics and Learning Health Sciences at the University of Michigan Medical School. 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, including serving as the director of the University of Michigan Medical School evidence-based medicine curriculum. 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 Scholars in Diagnostic Excellence. His research interests include augmenting clinical reasoning using artificial intelligence and machine learning, and implementation of safe and effective digital tools into clinical practice. He has received funding from the AMA and National Institutes of Health to further this research agenda. 

He has published articles in JAMA, Annals of Internal Medicine, Academic Medicine, the Journal of General Internal Medicine, Cell Reports, and more.