ArticleCureus2025
Performance of the ChatGPT-4o Language Model in Solving the Ophthalmology Specialization Exam.
Article in Cureus, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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
5 citing papers in PubMed.
- Article
- Article
- Comparison of GPT-4o With Human Performance in the Polish Vascular Surgery Specialty Examination.Cureus · 2025Article
- Assessment of the Ability of the ChatGPT-5 Model to Pass the Endocrinology Specialization Exam.Cureus · 2025Article
- Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
15 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background Artificial intelligence (AI), particularly language models such as ChatGPT, is gaining importance in medical education and knowledge assessment. Previous studies have demonstrated the growing effectiveness of AI in solving medical exams, including the Final Medical Examination (LEK) and Polish State Specialization Exam (PES) in various specialties, raising questions about its usefulness as a tool to support specialist training processes. Objective The aim of this study was to assess the effectiveness of the latest ChatGPT-4o model in solving the PES in ophthalmology. The analysis focused on the accuracy of the answers and the model's declared confidence level to evaluate its potential educational usefulness. Methods The study was based on the official PES ophthalmology exam (Spring 2024), consisting of 120 multiple-choice questions. The ChatGPT-4o model was familiarized with the exam regulations and questions, which were input in Polish. The effectiveness of the answers was assessed based on the Medical Education Center (CEM) answer key, as well as the model's declared confidence level (on a scale of 1 to 5). The questions were divided into clinical and theoretical categories. Data were analyzed statistically using the chi-square test and the Mann-Whitney U test. Results The model provided 94 correct answers (78.3%), exceeding the passing threshold. No significant difference in effectiveness was observed between clinical and non-clinical questions (p = 0.709). The analysis of the confidence level revealed that correct answers were significantly more often provided with higher confidence (p < 0.001), suggesting that the model's self-assessment could be an indicator of answer accuracy. Conclusions ChatGPT-4o demonstrated high effectiveness in the PES ophthalmology exam, confirming the potential of AI in specialist education. The confidence level of answers could serve as a useful tool in assessing the reliability of responses. Despite promising results, expert supervision and further research in various medical fields are necessary before wider implementation of AI models in medical education.
Indexed as
Identifiers
What OpenQuestion 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 OpenQuestion graph.