ArticleMedical journal of the Islamic Republic of Iran2025
ChatGPT's Performance on Iran's Medical Licensing Exams.
Article in Medical journal of the Islamic Republic of Iran, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers, 1 of them a synthesis that pooled it.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
3 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Performance of ChatGPT Across Different Versions in Medical Licensing Examinations Worldwide: Systematic Review and Meta-Analysis.Journal of medical Internet research · 2024Pooled it
- Performance of ChatGPT in medical licensing examinations in countries worldwide: A systematic review and meta-analysis protocol.PloS one · 2024Article
- Systematic review of ChatGPT accuracy and performance in Iran's medical licensing exams: A brief report.Journal of education and health promotion · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
12 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: A 175 billion parameter transformer architecture is used by OpenAI's ChatGPT language model to perform tasks requiring natural language processing. This study aims to evaluate the knowledge and interpretive abilities of ChatGPT on three types of Iranian medical license exams: basic sciences, pre-internship, and pre-residency. Methods: This comparative study involved administering three different levels of Iran's medical license exams, which included basic sciences, pre-internship, and pre-residency, to ChatGPT 3.5. Two versions of each exam were used, corresponding to the ChatGPT 3.5's internet access time: one during the access time and one after. These exams were inputted to ChatGPT in Persian and English. The accuracy and concordance of each question were extracted by two blinded adjudicators. Results: A total of 2210 questions, including 667 basic sciences, 763 pre-internship, and 780 pre-residency questions, were presented to ChatGPT in both English and Persian languages. Across all tests, the overall accuracy was found to be 48.5%, with an overall concordance of 91%. Notably, English questions exhibited higher accuracy and concordance rates, with 61.4% accuracy and 94.5% concordance, compared to 35.7% accuracy and 88.7% concordance for Persian questions. Conclusion: Our findings demonstrate that ChatGPT performs above the required passing scores on basic sciences and pre-internship exams. Moreover, ChatGPT could obtain the minimal score needed to apply for residency positions in Iran; however, it was lower than the applicants' mean scores. Significantly, the model showcases its ability to provide reasoning and contextual information in the majority of responses. These results provide compelling evidence for the potential use of ChatGPT in medical education.
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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.