SynthesisFrontiers in medicine2026
Development of AI competencies within the medical curriculum.
Synthesis in Frontiers in medicine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
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.
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What Socratic 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 Socratic graph.