Responsible AI integration in psychiatry GME requires staged introduction aligned with trainee developmental level, faculty engagement, human oversight in evaluation and decision-making, transparent communication of AI use, robust data governance, and prioritization of tools that deepen rather than replace human connection.
Artificial intelligence (AI) is being increasingly utilized in medical education, with growing interest in its potential to enhance learning, assessment, and faculty support across undergraduate, postgraduate, and continuing medical education. While much of the literature focuses on specialty training or technology-driven innovation in isolation, family medicine represents a distinct educational domain characterized by its breadth, clinical uncertainty, multimorbidity, longitudinal care, prevention, and shared decision-making. These features present specific educational challenges and opportunities for integrating AI-supported tools. This narrative review synthesizes peer-reviewed literature on the application of AI in medical education, specifically within the context of family medicine. This review utilized PubMed and a consensus search engine to identify relevant reviews, empirical studies, and articles that address AI-supported learning, simulation training, competency-based education, workplace assessment, faculty support, and educational governance. The evidence was integrated through a narrative synthesis, guided by the Scale for Assessment of Narrative Review Articles. The literature indicates that AI can facilitate personalized learning pathways, augment simulation-based training, and aid in the integration of longitudinal assessment data, particularly in educational contexts characterized by distributed supervision and diverse learner requirements. There is a paucity of evidence concerning the application of AI in high-stakes assessments, autonomous decision-making, and its long-term impact on professional identity formation. In all areas, the successful incorporation of AI necessitates robust educational governance, faculty oversight and alignment with fundamental educational principles. AI should be considered an augmentative educational technology that can enhance but not supplant human-centered teaching, supervision, and professional judgment. Future research should prioritize the validation of educational outcomes, the clarification of ethical safeguards, and the establishment of best practices for the responsible integration of AI into family medicine education.
C. Wiedermann, Anne Wiedermann, Hendrik Reismann· Journal of Medical Education...· 0 citations
Generative artificial intelligence (GenAI) chatbots, including ChatGPT and DeepSeek, are rapidly reshaping psychiatric education across the continuum from medical students to practicing psychiatrists. These tools offer learners unprecedented opportunities for personalized learning, clinical simulation, and research support. However, their integration also raises substantial risks related to data privacy, content reliability, over reliance on automation, and ethical and regulatory gaps. This perspective argues that neither uncritical adoption nor outright rejection is appropriate. Drawing on emerging evidence and our firsthand experience as psychiatric educators, we propose a balanced framework characterized by three interconnected principles: strong human oversight, comprehensive AI literacy training, and robust institutional governance. We outline five categories of risk, discuss potential benefits specific to psychiatric training, and offer six actionable institutional recommendations. The goal is not to deter innovation but to ensure that GenAI tools enhance rather than replace the clinical judgment, empathy, and professional accountability that lie at the heart of psychiatric practice.
Cong Zhou, Aoxue Zhang, Sen Li· Frontiers in Psychiatry· 0 citations
GenAI should be viewed as a powerful augmentative tool, not a replacement for human educators, and its successful integration will depend on leveraging its strengths to enhance efficiency and scalability while preserving the essential humanistic elements of medical practice through expert oversight and validation.
R. Xie, Bei-En Zhang, Lifeng Xiao· Frontiers in Medicine· 0 citations
It is argued that AI-focused CME should be designed as a longitudinal organizational learning system rather than a discrete instructional event, and implications are offered for technology integration, faculty development, and organizational learning in clinical education.
T. Murphy, Rob E. Carpenter· International Journal on Int...· 1 citation
Insight is provided into developing AI-ready medical education models that balance technical competence with humanistic values and factors influencing AI adoption in medical training, including performance expectancy, effort expectancy, social influence, and facilitating conditions.
T. Murphy, Ginger Vaughn, Rob E. Carpenter et al.· International Medical Educat...· 0 citations
The clinical impact of psychiatric AI will likely depend less on algorithmic novelty alone than on clearer clinical targets, prospective validation, implementation trials, patient‐centered evaluation, equity‐sensitive generalizability, and mental health–specific governance.
Esteban Zavaleta-Monestel, L. Herrera-Jiménez, Sofía Suárez-Sánchez et al.· Psychiatric Research and Cli...· 0 citations
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