Evidence map›Paper›PMID 41625731›Full record

ArticleFrontiers in medicine2025

Ten tips for utilizing AI to generate high quality OSCE stations in medical education.

Imran Zafar, Hadeel Aboueisha, Ibrahim Elhassan, Fouzia Shersad, Suleyman Ayhan Caliskan, Asma Fatima Syeda, Mohammed Al-Houqani, Mohi Eldin Magzoub

Abstract read
In one paragraph

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.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors.

Imran ZafarCollege of Medicine and Health Sciences, United Arab Emirates University, Al Ain, United Arab Emirates.
Hadeel AboueishaNational Institute of Health Specialties, Al Ain, United Arab Emirates.
Ibrahim ElhassanNational Institute of Health Specialties, Al Ain, United Arab Emirates.
Fouzia ShersadNational Institute of Health Specialties, Al Ain, United Arab Emirates.
Suleyman Ayhan CaliskanCollege of Medicine and Health Sciences, United Arab Emirates University, Al Ain, United Arab Emirates.
Asma Fatima SyedaNational Institute of Health Specialties, Al Ain, United Arab Emirates.
Mohammed Al-HouqaniCollege of Medicine and Health Sciences, United Arab Emirates University, Al Ain, United Arab Emirates.
Mohi Eldin MagzoubCollege of Medicine and Health Sciences, United Arab Emirates University, Al Ain, United Arab Emirates.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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.

Indexed as

AI generatedassessmentguidelinesmedical educationOSCE

Identifiers

PMID41625731
PMCPMC12855114

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.