ArticleHealth science reports2026
Examining the Performance of ChatGPT in Comprehensive Pre-Internship Exam: The Potential of Artificial Intelligence in Medical Education.
Article in Health science reports, 2026. 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
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
1 citing paper in PubMed.
- Examining the Performance of ChatGPT in Comprehensive Pre-Internship Exam: The Potential of Artificial Intelligence in Medical Education.Health science reports · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background and Aims: ChatGPT is a popular large language model with potential educational applications in medicine. However, its performance in standardized, multi-disciplinary medical exams has not been comprehensively assessed. This study evaluates ChatGPT's accuracy and quality in Iran's national medical pre-internship exam. Methods: We tested ChatGPT (GPT-3.5, May 3rd version) on 195 multiple-choice questions from the March 2022 Iranian pre-internship exam, covering 23 medical specialties. Questions with visual content were excluded. Each question was asked in a new chat to avoid memory bias. Responses were evaluated by 55 experts using a 5-point Likert scale and compared against the official answer key. Data were analyzed descriptively using SPSS. Results: ChatGPT answered 68.6% of questions correctly. Expert ratings averaged 4.23/5 (SD = 1.21), indicating good to excellent quality. Best-performing specialties included pharmacology (85.7%), otorhinolaryngology (83.3%), and dermatology (83.3%). Lower performance was observed in pulmonology (42.9%) and epidemiology (50%). Conclusion: ChatGPT shows promise as a supplemental educational tool in medical education, but its accuracy varies by specialty. Faculty guidance is essential to ensure responsible integration until further improvements and validations are made.
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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.