Artificial Intelligence and Large Language Models: A Case-Based, Peer-Teaching Workshop for Preclinical Medical...

Area of Science:

• Medical Education
• Artificial Intelligence in Healthcare
• Large Language Models

Background:

• Artificial intelligence (AI) tools are increasingly used in healthcare, impacting medical education.
• Formal curriculum integration of AI tools for medical students is limited.
• Preclerkship medical students require foundational knowledge of AI and large language models (LLMs).

Purpose of the Study:

• To introduce preclerkship medical students to the fundamentals of LLMs.
• To provide practical training on effectively utilizing AI tools for learning.
• To address the growing need for AI literacy in medical curricula.

Main Methods:

• A 60-minute lecture and 100-minute workshop were developed for second-year medical students.
• Interactive case studies on AI applications were incorporated.
• Peer-led teaching sessions were implemented in some groups.

Main Results:

• 168 students from Harvard Medical School and Harvard School of Dental Medicine participated.
• Pre- and post-session surveys (N=124 and N=62) showed a statistically significant increase in AI knowledge and self-efficacy (P < .01).
• Attitudes and behavioral intent towards AI showed mixed changes.

Conclusions:

• The developed AI and LLM session offers a framework for early medical education on these tools.
• Interactive exercises demonstrated effective AI tool utilization and risk assessment.
• Incorporating AI education is crucial for medical students as technology advances.