Artificial Intelligence in Medical Education: Implications for Undergraduate, Postgraduate, and Continuing Training in Family Medicine

Christian Josef Wiedermann; Anne Wiedermann; Hendrik Reismann

doi:10.62425/jmefm.1850362

Artificial Intelligence in Medical Education: Implications for Undergraduate, Postgraduate, and Continuing Training in Family Medicine

Abstract

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.

Keywords

Supporting Institution

Institute of General Practice and Public Health, Claudiana College of Health Professions, Bolzano (BZ), Italy

Ethical Statement

Not applicable

Thanks

Not applicable

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Details

Primary Language

English

Subjects

Family Medicine, Medical Education

Journal Section

Review

Authors

Christian Josef Wiedermann ^*
0000-0002-4639-8195
Italy

Anne Wiedermann
0009-0005-9813-5660
Germany

Hendrik Reismann
0009-0009-7093-9519
Germany

Publication Date

August 10, 2026

Submission Date

January 9, 2026

Acceptance Date

May 30, 2026

Published in Issue

Year 2026 Volume: 3 Number: 2

DOI

https://doi.org/10.62425/jmefm.1850362

IZ

https://izlik.org/JA27RS72XW

Cite

RIS / Bibtex

APA

Wiedermann, C. J., Wiedermann, A., & Reismann, H. (2026). Artificial Intelligence in Medical Education: Implications for Undergraduate, Postgraduate, and Continuing Training in Family Medicine. Journal of Medical Education and Family Medicine, 3(2), 86-98. https://doi.org/10.62425/jmefm.1850362

AMA

1.Wiedermann CJ, Wiedermann A, Reismann H. Artificial Intelligence in Medical Education: Implications for Undergraduate, Postgraduate, and Continuing Training in Family Medicine. J Med Educ Family Med. 2026;3(2):86-98. doi:10.62425/jmefm.1850362

Chicago

Wiedermann, Christian Josef, Anne Wiedermann, and Hendrik Reismann. 2026. “Artificial Intelligence in Medical Education: Implications for Undergraduate, Postgraduate, and Continuing Training in Family Medicine”. Journal of Medical Education and Family Medicine 3 (2): 86-98. https://doi.org/10.62425/jmefm.1850362.

EndNote

Wiedermann CJ, Wiedermann A, Reismann H (August 1, 2026) Artificial Intelligence in Medical Education: Implications for Undergraduate, Postgraduate, and Continuing Training in Family Medicine. Journal of Medical Education and Family Medicine 3 2 86–98.

IEEE

[1]C. J. Wiedermann, A. Wiedermann, and H. Reismann, “Artificial Intelligence in Medical Education: Implications for Undergraduate, Postgraduate, and Continuing Training in Family Medicine”, J Med Educ Family Med, vol. 3, no. 2, pp. 86–98, Aug. 2026, doi: 10.62425/jmefm.1850362.

ISNAD

Wiedermann, Christian Josef - Wiedermann, Anne - Reismann, Hendrik. “Artificial Intelligence in Medical Education: Implications for Undergraduate, Postgraduate, and Continuing Training in Family Medicine”. Journal of Medical Education and Family Medicine 3/2 (August 1, 2026): 86-98. https://doi.org/10.62425/jmefm.1850362.

JAMA

1.Wiedermann CJ, Wiedermann A, Reismann H. Artificial Intelligence in Medical Education: Implications for Undergraduate, Postgraduate, and Continuing Training in Family Medicine. J Med Educ Family Med. 2026;3:86–98.

MLA

Wiedermann, Christian Josef, et al. “Artificial Intelligence in Medical Education: Implications for Undergraduate, Postgraduate, and Continuing Training in Family Medicine”. Journal of Medical Education and Family Medicine, vol. 3, no. 2, Aug. 2026, pp. 86-98, doi:10.62425/jmefm.1850362.

Vancouver

1.Christian Josef Wiedermann, Anne Wiedermann, Hendrik Reismann. Artificial Intelligence in Medical Education: Implications for Undergraduate, Postgraduate, and Continuing Training in Family Medicine. J Med Educ Family Med. 2026 Aug. 1;3(2):86-98. doi:10.62425/jmefm.1850362