ArticleEuropean radiology experimental2025
Five advanced chatbots solving European Diploma in Radiology (EDiR) text-based questions: differences in performance and consistency.
Article in European radiology experimental, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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3 citing papers in PubMed.
- Quantitative estimation of pleural effusion volume on chest radiography: comparison of radiologists and a general-purpose multimodal AI model using CT as the reference standard.European radiology experimental · 2026Observational
- Comparative Performance of AI Models and Clinicians in Evidence-Based Cardiovascular Disease Management for People Living With HIV: Comparative Study.Journal of medical Internet research · 2026Article
- Can AI write reports like a radiologist? A blinded evaluation of large language model-generated lumbar spine MRI reports.European radiology experimental · 2026Observational
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7 authors.
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Abstract
backgroundWe compared the performance, confidence, and response consistency of five chatbots powered by large language models in solving European Diploma in Radiology (EDiR) text-based multiple-response questions.
methodsChatGPT-4o, ChatGPT-4o-mini, Copilot, Gemini, and Claude 3.5 Sonnet were tested using 52 text-based multiple-response questions from two previous EDiR sessions in two iterations. Chatbots were prompted to evaluate each answer as correct or incorrect and grade its confidence level on a scale of 0 (not confident at all) to 10 (most confident). Scores per question were calculated using a weighted formula that accounted for correct and incorrect answers (range 0.0-1.0).
resultsClaude 3.5 Sonnet achieved the highest score per question (0.84 ± 0.26, mean ± standard deviation) compared to ChatGPT-4o (0.76 ± 0.31), ChatGPT-4o-mini (0.64 ± 0.35), Copilot (0.62 ± 0.37), and Gemini (0.54 ± 0.39) (p < 0.001). A self-reported confidence in answering the questions was 9.0 ± 0.9 for Claude 3.5 Sonnet followed by ChatGPT-4o (8.7 ± 1.1), compared to ChatGPT-4o-mini (8.2 ± 1.3), Copilot (8.2 ± 2.2), and Gemini (8.2 ± 1.6, p < 0.001). Claude 3.5 Sonnet demonstrated superior consistency, changing responses in 5.4% of cases between the two iterations, compared to ChatGPT-4o (6.5%), ChatGPT-4o-mini (8.8%), Copilot (13.8%), and Gemini (18.5%). All chatbots outperformed human candidates from previous EDiR sessions, achieving a passing grade from this part of the examination.
conclusionClaude 3.5 Sonnet exhibited superior accuracy, confidence, and consistency, with ChatGPT-4o performing nearly as well. The variation in performance among the evaluated models was substantial. RELEVANCE STATEMENT: Variation in performance, consistency, and confidence among chatbots in solving EDiR test-based questions highlights the need for cautious deployment, particularly in high-stakes clinical and educational settings. KEY POINTS: Claude 3.5 Sonnet outperformed other chatbots in accuracy and response consistency. ChatGPT-4o ranked second, showing strong but slightly less reliable performance. All chatbots surpassed EDiR candidates in text-based EDiR questions.
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