Five advanced chatbots solving European Diploma in Radiology (EDiR) text-based questions: differences in performance and consistency - Pristoupil, Jakub; Oleaga, Laura; Junquero, Vanesa; Merino, Cristina; Ozbek, Suha Sureyya; Lambert, Lukas; European Society of Radiology (ESR) (European radiology experimental, 2025)
BACKGROUND: We 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. METHODS: ChatGPT-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). RESULTS: Claude 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. CONCLUSION: Claude 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.
Keywords: radiologie; Educational Measurement; generativní umělá inteligence; Education (medical); Generative Artificial Intelligence; Artificial intelligence; European Diploma in Radiology; lidé; Europe; Educational measurement; Evropa; Radiology; výuka - hodnocení; Humans