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.
Abstract
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.
Evidence more clearly shows changes in the design and availability of learning activities than durable improvement in learning, and future studies should prioritize active comparators, preregistration, model and prompt reporting, retention and transfer, adverse effects, and equitable implementation.
Jun-Sheng Zhao, Ke-Da Yang, Hainv Gao et al.· Frontiers in Education· 0 citations
This review aimed to map how AI has been integrated into higher education, classify applications using the SAMR model, analyze reported outcomes and challenges, and apply the FACETS framework to enhance comparability across studies.
M. Al-sheikh, Rania Zaini, Manahel Almulhem et al.· Frontiers in Education· 1 citation
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 he...
M. Morales-Cevallos, Diego Fabián Vique López, Catherine Hortensia Martínez Avalos et al.· Frontiers in Medicine· 0 citations
It is concluded that the integration of artificial intelligence into medical education should occur in an ethical and critical manner, preserving the human nature of clinical practice.
Carlos Alberto Severo Garcia, C. Tomasi, I. Toreti et al.· Revista Bioética· 0 citations
A conceptual model requiring prospective validation is proposed as a conceptual model requiring prospective validation, intended to move beyond ad hoc AI use toward structured, competency-based training that strengthens analytical reasoning, ethical judgment, and equity-oriented practice in AI-enabled public health sys...
H. Arab, Ghazal Assaad Mirdad, Maha Abdallah· Frontiers in Public Health· 0 citations
Introduction and purpose. Artificial intelligence has gained growing attention in medical education, especially since ChatGPT appeared in late 2022. Large language models proved capable of reaching or exceeding passing thresholds on USMLE-style questions or official practice materials, which sparked both enthusiasm and...
Paweł Klimas, Piotr Wysokiński, Sebastian Szmeja et al.· Journal of Education, Health...· 0 citations
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