Development of AI competencies within the medical curriculum
M. Morales-Cevallos Diego Fabián Vique LópezCatherine Hortensia Martínez AvalosBrigette Carolina Huaraca MorochoJhonatan David Silva AmezaMarwin Leandro Lavayen Leon
Aug 2026· Frontiers in Medicine· Vol 13· 0 citations· 71 references
Medicine
TL;DR
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.
Abstract
Introduction The rapid integration of artificial intelligence (AI) into healthcare is transforming clinical practice and redefining the competencies required of future physicians. As AI increasingly supports diagnostics, clinical decision-making, and healthcare management, medical curricula must evolve to ensure graduates possess the knowledge, skills, and ethical competencies necessary to interact effectively with AI-enabled systems. Methodology A systematic review was conducted following PRISMA guidelines. Literature searches were performed in PubMed, Scopus, and IEEE Xplore for studies published between 2020 and 2025. Eligibility criteria focused on original studies addressing AI competency development, curricular interventions, educational frameworks, and AI-related training within medical education. Following screening and eligibility assessment, 20 studies were included in the final synthesis. Results AI literacy was the most frequently identified competency, alongside clinical AI applications, data science, ethical reasoning, critical appraisal, and human–AI collaboration. Integrated and longitudinal curriculum models emerged as the predominant approaches for competency development. Active learning strategies, particularly simulations, workshops, project-based learning, and authentic clinical applications, were consistently associated with positive educational outcomes. Discussion The evidence indicates a transition from isolated AI educational initiatives toward competency-based and longitudinal curriculum integration. Effective AI education extends beyond technical literacy and increasingly incorporates ethical, clinical, and professional competencies necessary for responsible AI adoption in healthcare. Conclusion AI competencies should be recognized as a core component of the medical curriculum. Integrated, longitudinal, and active learning-based educational models provide the strongest foundation for preparing future physicians to critically evaluate, ethically govern, and effectively collaborate with AI technologies in clinical practice.
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
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
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
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
The findings highlight the need for longitudinal, program-wide integration of AI and digital health competencies, explicit alignment with recognised competency frameworks, and investment in educator capability to prepare graduates for safe and effective practice in increasingly AI-enabled healthcare environments.
Dianne Stratton-Maher, Thanveer Shaik, Yan Li et al.· Nurse Education Today· 0 citations
Purpose To explore the current status of medical AI educational programs and to review how these programs were developed, implemented, and improved. Methods Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR) guidelines, this study searched English articles from 2021 to April 2026 published in PubMed Central, Web of Science, Scopus, and MEDLINE databases. All curricula included in this review were implemented programs, not theoretical frameworks. Results Among the 7652 screened documents, 36 were identified. Data extraction focused on programs’ status, construction methods, and course frameworks. The number of programs has increased rapidly but remains small, with most in the pilot stage. Learners were predominantly medical students, with small class sizes. Curriculum development mainly relied on expert experience. Learning objectives mainly focused on the understanding level, but also covered advanced cognitive skills, and the content, covering AI principles, clinical applications, and ethics, demanded high cognitive engagement. Most programs were offered as modular elective courses in online or face-to-face formats, employing diverse teaching methods and activities. The learning outcomes and course evaluations mainly relied on student feedback. This review identified persistent gaps: the lack of standardized curriculum frameworks, affective learning objectives, and objective assessment tools; limited content on AI development, applications, collaboration, communication; weak integration with existing curricula; and insufficient involvement of learning designers. Conclusion Medical AI education programs are still in the initial stage. There is a need to accelerate curriculum development pathways, enhance practical skill trainings and affective competency developments, and establish effective instructional design and evaluation systems.
Yue Wang, He Wang, Ting Wang et al.· Journal of Multidisciplinary...· 0 citations
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