AI in Medical Education: A Grants Program to Advance Innovation in Medical Education

AI in Medical Education: A Grants Program to Advance Innovation in Medical Education is sponsored by Josiah Macy Jr. Foundation. This program funds demonstration projects that advance understanding of responsible, effective, and ethical use of AI in medical education. It supports initiatives like AI-driven assessment of surgical skills and AI-enhanced feedback systems for communication.

The Macy Foundation - Artificial Intelligence in Medical Education Artificial Intelligence in Medical Education Watch the Innovating Med Ed with AI: Insights from the Macy Report Webinar (Hosted by AAMC) Watch the AI in Med Ed Conference Recommendations Webinar (Hosted by AAMC) View AAMC's AI in Academic Medicine Webinar Series 2024 Conference on AI in Medical Education AI in Medical Education: A Grants Program to Advance Innovation in Medical Education As a follow-up to the 2024 Macy conference on AI in Medical Education , the Josiah Macy Jr. Foundation announced a special initiative to fund demonstration projects that advance our understanding of what responsible, effective, and ethical use of AI in medical education will look like in the immediate future.

AI in Medical Education: A Grants Program to Advance Innovation in Medical Education provides support for three demonstration projects, each receiving up to $200,000 over two years. Please see the descriptions of the awarded projects below . materials that emanated from the conference, please visit the Macy Conferences section of the website.

AI in Medical Education: A Grants Program to Advance Innovation in Medical Education Artificial Intelligence Driven Assessment of Resident Surgical Skill Using Video Analysis of Incision Closures in Open Surgery Beth Israel Deaconess Medical Center The grant will leverage computer vision and AI methods to provide automated assessments of surgical skill in open procedures for surgical residents.

Automated video analytics will be captured during incision closures from traditional room video and egocentric recording from smart glasses. The model’s outputs will then be available on a resident-facing web platform designed to support feedback and longitudinal skill tracking. The overall goal is to refine AI-based assessment for open procedures to augment feedback from educators and encourage targeted practice for surgical trainees.

PI: Gabriel Brat, MD, Assistant Professor of Surgery, Beth Israel Deaconess Medical Center, Assistant Professor of Biomedical Informatics, Harvard Medical School Communication Compass: AI-Enhanced Feedback System NYU Grossman School of Medicine Effective patient-physician communication is crucial for high-quality healthcare, but challenging to teach and assess consistently.

Traditional methods often lack the ability to provide timely, objective feedback, leading to gaps in learners' communication skills. To address this, we are developing Communication Compass, an AI-powered system designed to offer personalized, near real-time feedback on learners' communication skills by analyzing ambient audio from patient-physician interactions.

This system will use advanced AI to assess communication across various dimensions and provide structured, rigorously-validated feedback with real examples. We will test this system’s impact on both educational outcomes and patient care through a randomized trial.

Ultimately, Communication Compass aims to create an ethical, scalable framework for improving communication skills in medical education, benefiting learners and patients alike.

PIs: Yuliya Yoncheva, PhD, Research Assistant Professor, Institute for Innovations in Medical Education; Jesse Burk-Rafel, MD, MRes, Director of Research, Institute for Innovations in Medical Education Teaching Future Doctors to Team with AI: A Social Science Approach to Developing and Evaluating Training Methods for Clinical-AI Collaboration Across the ARiSE Network Stanford Center for Biomedical Informatics Research Large language models (LLMs) like ChatGPT are one of the fastest adopted medical technologies in history, now routinely appearing in both clinics and medical classrooms alike.

The stakes are high – multiple studies now show that high-performing artificial intelligence technologies don’t always improve doctor performance. In order to fully recognize the health benefits of these powerful AI systems, medical educators will need to figure out how to better train doctors to work with AI.

The ARiSE Research Network, based at Beth Israel Deaconess Medical Center / Harvard Medical School and Stanford University, will use gold standard social science methodologies to understand how doctors are currently using AI in their clinical care.

They will then run national randomized trials to determine which educational strategies truly boost doctor-AI teamwork in important clinical tasks like diagnosing illnesses, explaining care plans to patients and coordinating treatment. The curricula and assessments from this project will be released openly so medical schools and hospitals around the world can train future doctors to use AI confidently while keeping patients safe.

PIs: Jonathan H. Chen, MD, PhD, Assistant Professor & Director for Medical Education in Artificial Intelligence, Stanford Center for Biomedical Informatics Research; Adam Rodman, MD, MPH, FACP, Hospitalist, Beth Israel Deaconess Medical Center; Assistant Professor, Harvard Medical School Learn more about Our Grantees