ArticleScientific reports2026
Comparison of the performance of ChatGPT-5, Gemini 3, Copilot, Perplexity, and medical students in answering neurology questions: a cross-sectional study.
Article in Scientific reports, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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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
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Who cites it
2 citing papers in PubMed.
- Comprehensive Evaluation of Large Language Models on Four Core Medical School Courses: A Cross-Sectional Comparative Study.Advances in medical education and practice · 2026Article
- Article
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Authors and funding
5 authors.
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Abstract
Large language model (LLM)-based chatbots have been utilized across various healthcare domains and have garnered substantial attention. This study aimed to evaluate and compare the performance of several LLM-based chatbots with that of medical students in responding to neurology questions. This cross-sectional study, conducted in December 2025 in Iran. ChatGPT-5, Gemini 3, Copilot 2025, Perplexity, and 20 medical students responded to a neurology questionnaire. A confusion matrix was utilized to analyze the data. In this regard, four metrics—sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV)—as well as overall accuracy were calculated. Moreover, correlations examined chatbot performance against question characteristics (word count, context, format, type, modality, language). The study revealed that overall performance metrics for the evaluated chatbots significantly outperformed those of medical students (p < 0.001). Among the evaluated chatbots, Copilot exhibited superior performance (0.88), followed by ChatGPT-5 (0.86), in terms of accuracy. Meanwhile, quantitative question types were associated with a significant reduction in chatbot performance (r = 0.470, p = 0.001). The study findings presented valuable insights results particularly pertinent to neurology, where chatbots can serve as supplementary tools for practitioners, enhancing diagnostic accuracy and clinical decision-making while adhering to established ethical standards. However, further research is required to provide more precise insights, particularly with a larger sample size of human participants.
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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.