ReviewAdvances in medical education and practice2026
Application of Artificial Intelligence in Medical Education: A Systematic and Narrative Review of Pedagogical Potential and Ethical Implications.
Review in Advances in medical education and practice, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
2 citing papers in PubMed.
- AI in UK Medical Education: A Framework for Curriculum Reform.JMIR medical education · 2026Article
- Artificial intelligence in anesthesiology education: transformative applications, challenges, and future perspectives.Frontiers in medicine · 2026Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Artificial intelligence (AI) is rapidly transforming medical education through large language models (LLMs), virtual reality (VR), intelligent tutoring systems, and decision-support platforms. These tools enable adaptive instruction, immersive simulation, and real-time feedback, showing strong potential to improve outcomes across health professions training. To explore both opportunities and risks, we conducted a systematic review of PubMed, EMBASE, Web of Science, and Scopus for English-language studies published between January 2015 and May 2025, following the PRISMA framework. Nineteen studies met eligibility criteria. AI modalities identified included LLMs such as ChatGPT, VR-based simulation systems, automated tutoring platforms, and clinical decision-support tools, spanning specialties including radiology, surgery, and psychiatry. Across contexts, AI enhanced examination performance, procedural competence, self-directed learning, engagement, and motivation relative to traditional methods. Students and faculty expressed strong interest and optimism but reported limited formal AI training, favoring interactive practice over didactic lectures. Despite these benefits, concerns consistently emerged regarding algorithmic bias, inaccuracy, data security, and the necessity of human oversight in educational and clinical settings. Ethical issues such as job displacement, the erosion of humanistic care, and the impact on the patient-physician relationship were also highlighted. Limited formal AI training, uneven institutional readiness, and gaps in faculty expertise were common challenges across regions.To harness its transformative potential responsibly, investment is required in faculty development, structured curricula addressing both technical and ethical competencies, and governance frameworks that ensure equitable, transparent, and accountable use. Properly integrated, AI can not only personalize learning and expand access but also support a more inclusive and ethically grounded vision for the future of medical education.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.