Jul 2026· International Journal on Integrating Technology in Education· Vol 15, pp. 29-43· 1 citation· 34 references
TL;DR
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.
Abstract
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.
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
Evidence indicates a transition from isolated AI educational initiatives toward competency-based and longitudinal curriculum integration and effective AI education extends beyond technical literacy and increasingly incorporates ethical, clinical, and professional competencies necessary for responsible AI adoption in healthcare.
M. Morales-Cevallos, Diego Fabián Vique López, Catherine Hortensia Martínez Avalos et al.· Frontiers in Medicine· 0 citations
Artificial intelligence (AI) is entering clinical practice through decision-support systems, predictive tools, generative models, and clinical documentation solutions. Since February 2025, the European AI Act has required providers and deployers of AI systems to ensure that staff and other people operating or using such systems on their behalf have an adequate level of AI literacy. This creates a new educational requirement: physicians need to acquire minimum competencies to use, appraise, supervise, and reject AI outputs when appropriate. This state-of-the-art review examined AI competencies relevant to postgraduate medical training, focusing on literature published since 2022. The reviewed literature suggests thematic convergence around six core domains: foundational AI literacy, critical appraisal of AI tools, safe clinical application, ethics/law/governance, patient communication, and health data literacy. Other domains, such as institutional implementation, multidisciplinary collaboration, and AI leadership, appear as intermediate or advanced competencies. We propose a three-tier curricular organization: baseline competencies for all physicians; proficient competencies for physicians involved in local appraisal and implementation of AI systems; and advanced competencies for clinician-scientists, institutional leaders, and professionals with formal responsibilities in AI governance. In the Portuguese context, this structure may support the development of transversal training for residents and specialists, aligned with technological change, European regulatory requirements, and the need to preserve human clinical responsibility. This proposal should be understood as a conceptual basis for multidisciplinary consensus validation.
João Frutuoso, A. Maria, Helena Donato et al.· Acta Médica Portuguesa· 0 citations
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
This curriculum offers a generalizable, no-cost model for closing the AI evaluation skills gap in healthcare education, combining interactive correction with a mandatory, learner-defined capstone project.
Hiba Hamdar· Journal of Medical Education...· 0 citations
This structured, task-level guidance positions nursing education to lead intentional AI integration while preparing students for AI-enabled practice environments.
