ArticleFrontiers in medicine2025
Ten tips for utilizing AI to generate high quality OSCE stations in medical education.
Article in Frontiers in medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
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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.
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Who cites it
1 citing paper in PubMed.
- Generative artificial intelligence in medical education: from knowledge assessment to clinical reasoning and professional competence.Frontiers in medicine · 2026Review
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Authors and funding
8 authors.
Funding
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Abstract
Artificial intelligence (AI) is reshaping medical education, offering novel solutions to long-standing challenges in clinical assessment design. One of the most resource-intensive components of assessment is the development of high-quality Objective Structured Clinical Examination (OSCE) stations that are valid, reliable, and aligned with curricular outcomes. Traditional approaches to OSCE case creation are time-consuming, vulnerable to inconsistency, and difficult to scale. AI, particularly large language models, has emerged as a powerful tool for generating realistic, diverse, and customizable clinical scenarios. However, its safe and effective use in high-stakes examinations requires structured guidance and faculty oversight. This manuscript presents 10 evidence-informed, practical tips for leveraging AI to generate OSCE stations that are pedagogically sound and clinically authentic. These tips draw on focused literature review, experiential insights from AI-assisted case development, and consensus from medical education experts. Together, they provide a framework for integrating AI into assessment workflows while ensuring quality, fairness, security, and ethical compliance. By following these recommendations, educators and examination committees can enhance efficiency, maintain validity, and prepare learners for the complexities of modern clinical practice.
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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.