Use of AI by Family Medicine Trainers in Training: A Narrative Review

Abstract

Background: As artificial intelligence, especially generative models, rapidly transforms clinical practice, Family Medicine trainers are experiencing firsthand the pressing need to rethink traditional education. Trainers are not just content experts; they now prepare residents to become mindful users and ethical stewards of these evolving technologies, striking a balance between innovation and the enduring values of human care. Methods: This narrative review synthesizes current literature regarding the impact of AI on Family Medicine trainers, focusing on three core functions: pedagogy, ethical governance, and the preservation of humanistic care. Key areas reviewed include AI applications in objective assessment, personalized learning, the mitigation of algorithmic and empathy bias, and the necessary competencies for faculty development. Results: AI fundamentally transforms the educational landscape by enabling scalable, objective assessment of complex skills, such as clinical reasoning documentation, a process previously infeasible with human rating alone (Schaefer et al., 2024). However, this adoption carries significant risks, including the potential for AI outputs to exhibit social and empathy biases, and the critical threat of over-reliance leading to the erosion of foundational clinical skills. The trainer's role is shifting from content dispenser to essential ethical gatekeeper and humanistic coach. Conclusions and Recommendations: Responsible AI integration requires a strategic, two-pronged approach. First, faculty training must mandate Proficient-level AI literacy, emphasizing the critical appraisal of algorithms and the active detection of bias (EdgePoint Learning, 2024). Second, programs must implement structural policies, such as the intentional redirection of AI-free time toward high-value humanistic activities like shared decision-making training, to safeguard the core physician-patient relationship against technological overreach.

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