The potential for AI to revolutionize approaches to education in emergency medicine (EM) is profound, reflected in the recent explosion of interest and literature exploring its use. Important preliminary applications have included leveraging machine learning and natural language processing: 1) to facilitate recruitment (via Chatbots) and selection (through holistic review of applicants to residency and fellowship); 2) to provide precision education (through personalized content recommendations, tailored learning experiences, intelligent tutoring systems, and virtual patient simulators); and 3) to improve assessment of and for learning (by tracking competency progression, providing predictive analytics, enhancing feedback, and automating case and procedure logs.) The presenters will draw on their recent experience conducting a systematic review of AI applications in medical education, as well as their personal experience implementing AI, to provide a summary of current applications and innovations most relevant to EM, highlighting their strengths, weaknesses and future directions. Participants will leave with an enhanced understanding of the current landscape of AI applications in medical education, preparing them to adapt, innovate and lead this technological revolution in their home programs. This didactic will also lay a foundation for a future consensus conference exploring this topic.
Presenters:
Michelle Daniel, MD, MHPE
Maxwell Spadafore, MD
Mary R. C Haas, MD, MHPE
Carl Preiksaitis, MD
