Accepted for/Published in: JMIR Medical Education
Date Submitted: Mar 24, 2024
Date Accepted: Jan 2, 2025
Generative Artificial Intelligence in Medical Education - Policies and Training at US Osteopathic Medical Schools: Descriptive Cross-Sectional Survey
ABSTRACT
Background:
Interest has recently increased in Generative AI (GenAI), a subset of artificial intelligence that can create new content. Though the publicly available GenAI tools are not specifically trained in the medical domain, they have demonstrated proficiency in a wide range of medical assessments. The future integration of GenAI in medicine remains unknown. However, the rapid availability of GenAI with a chat interface, and the potential risks and benefits are the focus of great interest. As with any significant medical advancement or change, medical schools must adapt their curricula to equip students with the skills necessary to become successful physicians. Additionally, medical schools must ensure faculty members have the skills to harness these new opportunities to increase their effectiveness as educators. How medical schools currently fulfill their responsibilities is unclear. Colleges of Osteopathic Medicine (COM) in the United States currently train a significant proportion of the total number of medical students. These COMs are in academic settings ranging from large public research universities to small private institutions. Therefore, studying COMs will offer a representative sample of the current GenAI integration in medical education.
Objective:
This study aims to describe the policies and training regarding the specific aspect of Generative Artificial Intelligence (GenAI) in US Colleges of Osteopathic Medicine (COMs), targeting students, faculty, and administrators.
Methods:
Online surveys were sent to Deans and Student Government Association (SGA) Presidents of the main campuses of fully accredited US Colleges of Osteopathic Medicine (COM). The Dean survey included questions regarding current and planned policies and training related to GenAI for students, faculty, and administrators. The SGA President survey included only questions related to current student policies and training.
Results:
Responses were received from 26 (81%) COMs surveyed. This included 15 (47%) of the Deans and 16 (50%) SGA Presidents. Most COMs did not have a policy on the student use of GenAI, as reported by the Dean (93%) and SGA President (88%). Of the COMs with no policy, 79% had no formal plans for policy development. Only one COM had training for students, focusing entirely on the ethics of using GenAI. Most COMs had no formal plans to provide mandatory (78%) or elective (73%) training. No COM had GenAI policies for faculty/administrators. Eighty percent had no formal plans for policy development. Five (33.3%) COMs had Faculty/Administrator GenAI training. Except for exam question development, there was no training to increase faculty/administrator capabilities and efficiency or to decrease their workload.
Conclusions:
The survey revealed that most COMs lack GenAI policies and training for students, faculty, and administrators. The few institutions with policies or training were extremely limited in scope. Most institutions without current training or policies had no formal plans for development. The lack of current policies and training initiatives suggests inadequate preparedness for integrating GenAI into the medical school environment; therefore, relegating the responsibility for ethical guidance and training to the individual COM member.
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