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
Generative artificial intelligence (GenAI), particularly large language models (LLMs), is poised to fundamentally transform medical education. Based on a structured literature search of PubMed, Scopus, Web of Science, and Google Scholar, this review synthesizes current evidence on the applications, capabilities, and limitations of GenAI across the medical training continuum. Advanced LLMs demonstrate a formidable command of medical knowledge, consistently achieving passing scores on standardized licensing examinations, with GPT-4 and domain-specific models like Ortho GPT showing particular proficiency. As versatile teaching tools, these models can generate high-quality assessment materials, provide personalized on-demand tutoring, and power interactive virtual patients for clinical reasoning practice. However, this potential is tempered by significant challenges, including a propensity for “hallucinations,” embedded biases that can perpetuate health inequities, linguistic performance disparities, and a fundamental gap in flexible, adaptive clinical reasoning. Integration also raises critical concerns regarding academic integrity, potential over-reliance leading to deskilling, and data privacy. Responsible adoption requires a structured approach encompassing the development of tiered AI competency frameworks, blended curricular integration, dedicated faculty development, and a rigorous research agenda focused on longitudinal learning outcomes. Ultimately, GenAI should be viewed as a powerful augmentative tool, not a replacement for human educators. 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
Artificial intelligence (AI) is increasingly embedded in clinical decision support, documentation, imaging, predictive analytics, and workflow management. However, the educational systems responsible for preparing practicing physicians to use AI have not fully addressed the interpretive, ethical, and organizational demands of AI-supported care. This qualitative case study examined how continuing medical education (CME) shapes physicians’ readiness to integrate AI into clinical practice. Guided by the Unified Theory of Acceptance and Use of Technology (UTAUT) and human resource development (HRD), the study analyzed semi-structured interviews with 14 physicians from diverse specialties within a large academic health system. Organizational artifacts, including CME announcements, internal communications, and evaluation materials, were also examined to support triangulation. Findings indicate that CME was most influential when it framed AI as support for clinical attention rather than as decision authority, developed interpretive judgment, reinforced physician accountability, connected formal instruction to informal workplace learning, aligned with clinical workflow, and supported trust calibration over time. The study argues that AI-focused CME should be designed as a longitudinal organizational learning system rather than a discrete instructional event. 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
Artificial intelligence (AI), treated in this study as an umbrella term for AI-enabled clinical and educational technologies rather than as a single platform, is reshaping medical education, including how diagnostic skills, treatment planning, and patient care are taught. This study examines AI integration in medical education through the perceptions and readiness of clinical educators. Guided by the Unified Theory of Acceptance and Use of Technology, the study explores factors influencing AI adoption in medical training, including performance expectancy, effort expectancy, social influence, and facilitating conditions. In this exploratory study, semi-structured interviews were conducted with 15 clinical educators in the south-central United States who supervise third-year medical students. Findings suggested six recurring themes: the technological learning curve, the need for hands-on learning, institutional support, mentorship, preservation of human elements, and generational differences in comfort with AI. While some AI-enabled applications may support adaptive and personalized learning, educators expressed concerns about maintaining empathy, patient interaction, and human-centered care. The findings suggest that effective AI integration may require strategic institutional support, ongoing training, and pedagogical change. This study provides insight into developing AI-ready medical education models that balance technical competence with humanistic values.
T. Murphy, Ginger Vaughn, Rob E. Carpenter et al.· International Medical Educat...· 0 citations
Objective This review examined the historical development of artificial intelligence (AI) in psychiatry from 1972 to 2025 and identified persistent barriers to clinical translation. Method The review used a structured source‐identification approach across PubMed/MEDLINE, Web of Science, Google Scholar, ScienceDirect, citation tracking, and targeted historical searches. It synthesized sources qualitatively and organized them chronologically. A structured evidence table summarized representative milestones by era, AI paradigm, clinical task, data modality, validation approach, implementation status, and translational limitation. Results Psychiatric AI evolved from symbolic simulation and rule‐based expert systems to connectionist models, supervised machine learning, computational psychiatry, digital phenotyping, multimodal monitoring, digital mental health tools, and large language models. Despite increasing computational sophistication, recurring barriers persisted, including uncertain target validity, diagnostic heterogeneity, limited external validation, poor transportability, interpretability challenges, workflow integration, equity, patient trust, and governance. Across eras, technical progress was cyclical rather than linear, with successive waves reproducing unresolved clinical and implementation challenges. Conclusions 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. Relevance to Clinical Practice For routine psychiatric care, AI tools require evidence of clinical validity, transportability, workflow compatibility, patient acceptability, equity, and appropriate governance rather than technical performance alone.
Esteban Zavaleta-Monestel, L. Herrera-Jiménez, Sofía Suárez-Sánchez et al.· Psychiatric Research and Cli...· 0 citations