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Introducing AI as members of script concordance test expert reference panel

A comparative analysis

Bibliographic Data

ID21447441
AuthorsMoataz A Sallam (0000-0003-4759-4849, Suez Canal University, corresponding author), Enjy Abouzeid (0000-0002-9431-6019, Suez Canal University)
Year2025
Volume47
Issue10
Pages1637-1644
Publication date2025-10-03
Open AccessNo
TypeARTICLE
VenueMedical Teacher (JOURNAL)
Journal identifiersISSN: 0142-159X • E-ISSN: 1466-187X
PublisherInforma UK Limited (PUBLISHER • GB)
DOI10.1080/0142159x.2025.2473620
PMID40056088
OpenAlexW4408246974
LanguageEN
Citations received2
References cited26

BACKGROUND: The Script Concordance Test (SCT) is increasingly used in professional development to assess clinical reasoning, with linear progression in SCT performance observed as clinical experience increases. One challenge in implementing SCT is the potential burnout of expert reference panel (ERP) members. To address this, we introduced ChatGPT as panel members. The aim was to enhance the efficiency of SCT creation while maintaining educational content quality and to explore the effectiveness of different models as reference panels. METHODOLOGY: A quasi-experimental comparative design was employed, involving all undergraduate medical students and faculty members enrolled in the Ophthalmology clerkship. Two groups involved Traditional ERP which consisted of 15 experts, diversified in clinical experience: 5 senior residents, 5 lecturers, and 5 professors and AI-Generated ERP which is a panel generated using ChatGPT and o1 preview, designed to mirror diverse clinical opinions based on varying experience levels. RESULTS: Experts consistently achieved the highest mean scores across most vignettes, with ChatGPT-4 and o1 scores generally slightly lower. Notably, the o1 mean scores were closer to those of experts compared to ChatGPT-4. Significant differences were observed between ChatGPT-4 and o1 scores in certain vignettes. These values indicate a strong level of consistency, suggesting that both experts and AI models provided highly reliable ratings. CONCLUSION: These findings suggest that while AI models cannot replace human experts, they can be effectively used to train students, enhance reasoning skills, and help narrow the gap between student and expert performance

Concordance · Natural language processing · Test (biology) · Artificial Intelligence in Healthcare and Education · Clinical Reasoning and Diagnostic Skills · Computer Science · Innovations in Medical Education · Medicine · Psychology

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Citations per year2
Citation span2025 - 2025 (1)
Highly citedNo

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