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Piloting an AI Introduction Program for Incoming Medical Students: A Novel Approach to Medical Education
Kenny T L Ta1, Ayush Patel1, Nathan Keller1
1University of Minnesota Medical School, Minneapolis, MN 55455 USA.
Background:
Artificial intelligence (AI) is increasingly recognized for its potential to enhance personalized learning and clinical decision-making in medical education. However, limited integration of AI into medical curricula may leave students feeling underprepared for AI-driven healthcare. This study piloted an AI education program for first-year medical students, assessing post-session perceptions of knowledge, confidence, and perspectives regarding AI in medicine.
Methods:
An interactive AI session was conducted for 241 first-year medical students at University of Minnesota Medical School. The session included components of lecture, live demonstrations, and exercises with AI tools such as ChatGPT, Copilot, and Gemini, where students applied AI to various educational and clinical scenarios. Discussions included potential benefits, harms, and limitations including ethical implications, bias and privacy concerns. A post-session survey captured students' understanding, perceived utility, and confidence regarding AI use in education and in medicine.
Results:
Of 241 medical students, 180 attended the session, and 92 of attendees (51%) completed the post-session survey. Findings showed that a majority recognized AI's value for enhancing study efficiency and diagnostic support, with over 50% indicating increased confidence in incorporating AI tools into their education and possible future practice. Concerns emerged around AI reliability, potential over-reliance, and ethical issues, especially regarding data privacy and bias.
Conclusions:
This pilot study provides preliminary evidence supporting the early introduction of AI concepts in medical education. Students' ethical concerns highlight the need for balanced curricula that combine foundational AI literacy with critical ethical understanding. The study's descriptive, post-session design limits causal inference; future work should incorporate pre- and post-assessments and longitudinal follow-up to evaluate the sustained impact of AI education on clinical competencies and ethical awareness.