ArticleScientific reports2025
A bi-linguistic comparative analysis of ChatGPT-4, Gemini, and Claude performance on Polish medical-dental final examinations.
Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Accuracy and Temporal Consistency of ChatGPT and Gemini in Responding to Textbook and Patient-Oriented Dental Bleaching Questions: A Multi-Session Comparative Study.Journal of esthetic and restorative dentistry : official publication of the American Academy of Esthetic Dentistry ... [et al.] · 2026Article
- Diagnostic Performance and Workup Efficiency of Large Language Models in Secondary Hypertension: A Blinded Comparative Study.Diagnostics (Basel, Switzerland) · 2026Article
- Are chatbots reliable sources of information regarding fluoride in pediatric dentistry?BMC oral health · 2026Article
- Bi-linguistic performance of large language models in multimodal analysis for differentiating jawbone-destroying malignancy from osteomyelitis.BMC oral health · 2026Article
Corrections and comments
- Erratum issued
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
In the realm of medical education, the utility of chatbots is being explored with growing interest. One pertinent area of investigation is the performance of these models on standardized medical examinations, which are crucial for certifying the knowledge and readiness of healthcare professionals. In Poland, dental and medical students have to pass crucial exams known as LDEK (Medical-Dental Final Examination) and LEK (Medical Final Examination) exams respectively. The primary objective of this study was to conduct a comparative analysis of chatbots: ChatGPT-4, Gemini and Claude to evaluate their accuracy in answering exam questions of the LDEK and the Medical-Dental Verification Examination (LDEW), using queries in both English and Polish. The analysis of Generalized Linear Mixed-Effects Model, which compared chatbots within question groups, showed that the chatbot Claude achieved the highest probability of accuracy for all question groups except the area of prosthetic dentistry compared to ChatGPT-4 and Gemini. In addition, the probability of a correct answer to questions in the field of integrated medicine was higher than in the field of dentistry for all chatbots in both prompt languages. Our results demonstrated that Claude achieved the highest accuracy in all areas analysed and outperformed other chatbots. This suggests that Claude has significant potential to support the medical education of dental students. This study showed that the performance of chatbots varied depending on the prompt language and the specific field. This highlights the importance of considering language and specialty when selecting a chatbot for educational purposes.
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