Framework for Training Medical Students in the Safe and Effective Use of Artificial Intelligence in Primary Care

The use of artificial intelligence (AI) in healthcare, particularly in primary care settings, is expanding, introducing new opportunities and potential risks across the areas of charting, summarization, and diagnosis. As reliance on AI recommendations increases, understanding when and why to question these outputs becomes necessary for maintaining clinical safety and trust. Generally, AI systems can make mistakes due to limited or biased training data, misinterpretation of context, or errors in algorithmic reasoning. This research explores how family practice physicians use and evaluate AI tools in everyday care, highlighting the gap between rapid technological growth and insufficient training. Personal interviews and a broad survey of family practice physicians were conducted to understand AI use cases, perspectives, and risk awareness, particularly in preventative care. As well as collaboration with medical students to assess their current knowledge and educational needs. Analysis of these findings revealed key gaps in the current curriculum. Through benchmarking with other curricula and AI professionals, the research produced a plan for the needed syllabus, which was reviewed by current physicians and discussed with medical students. This syllabus aims to prepare future medical providers to critically engage with AI by understanding its limitations and strengths.