ArticleFrontiers in medicine2025
Evaluating the performance of ChatGPT in patient consultation and image-based preliminary diagnosis in thyroid eye disease.
Article in Frontiers in medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Evaluating the performance of ChatGPT in responding to myotonic dystrophy type 1 patient inquiries: a specialist physician-based study.BMC neurology · 2026Article
- Evaluating and enhancing the performance of large language models in thyroid eye disease through customization and Chain-of-Thought strategies.Scientific reports · 2026Article
- Promise and pitfalls of AI chatbots in complex decision-making for thyroid nodules and papillary thyroid cancer.European thyroid journal · 2026Article
- Multimodal reasoning agent for enhanced ophthalmic decision-making: a preliminary real-world clinical validation.Frontiers in cell and developmental biology · 2025Article
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9 authors.
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Abstract
Background: The emergence of Large Language Model (LLM) chatbots, such as ChatGPT, has great promise for enhancing healthcare practice. Online consultation, accurate pre-diagnosis, and clinical efforts are of fundamental importance for the patient-oriented management system. Objective: This cross-sectional study aims to evaluate the performance of ChatGPT in inquiries across ophthalmic domains and to focus on Thyroid Eye Disease (TED) consultation and image-based preliminary diagnosis in a non-English language. Methods: We obtained frequently consulted clinical inquiries from a published reference based on patient consultation data, titled Results: For common TED consultation questions, ChatGPT-4o delivered more accurate information with logical consistency, adhering to a structured format of disease definition, detailed sections, and summarized conclusions. Notably, the answers generated by ChatGPT-4o were rated higher than those of ChatGPT-4 and the professor, with accuracy (4.33 [0.69]), comprehensiveness (4.17 [0.75]), conciseness (4.12 [0.77]), and satisfaction (4.28 [0.70]). The characteristics of the evaluators, the response variables, and other quality scores were all correlated with overall satisfaction levels. Based on several facial images, ChatGPT-4 twice failed to make diagnoses because of lacking characteristic symptoms or a complete medical history, whereas ChatGPT-4o accurately identified the pathologic conditions in 31.25% of cases (95% confidence interval, CI: 11.02-58.66%). Furthermore, in combination with CT images, ChatGPT-4o performed comparably to the professor in terms of diagnosis accuracy (87.5, 95% CI 61.65-98.45%). Conclusion: ChatGPT-4o excelled in comprehensive and satisfactory patient consultation and imaging interpretation, indicating the potential to improve clinical practice efficiency. However, limitations in disinformation management and legal permissions remain major concerns, which require further investigation in clinical practice.
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