Cornelius James, MD
Assistant Professor of Internal Medicine, Pediatrics and Learning Health Sciences
University of Michigan
Medical School
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.

Presenting at the Nexus Summit:

Seminar Description: At a recent workshop run by the National Academies’ Global Forum on Innovation in Health Professional Education (HPE), educators and practitioners with varying levels of understanding of artificial intelligence (AI), gathered to listen to and learn from those with experience in applying AI in HPE. Discussions were based on a framework proposed by Lomis et al. (2021) that emphasized collective action of educators across professions to implement and optimize the use of AI while mitigating unintended consequences. This discussion, and the wisdom drawn from the audience’s…