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聊天机器人与人类专家:评估聊天机器人在葡萄膜炎中的诊断性能以及人工智能在眼科中的应用前景。

Chatbots Vs. Human Experts: Evaluating Diagnostic Performance of Chatbots in Uveitis and the Perspectives on AI Adoption in Ophthalmology.

机构信息

Lee Kong Chiang School of Medicine, Nanyang Technological University, Singapore, Singapore.

Retina and Uvea Services, Sadguru Netra Chikitsalaya, Chitrakoot, India.

出版信息

Ocul Immunol Inflamm. 2024 Oct;32(8):1591-1598. doi: 10.1080/09273948.2023.2266730. Epub 2023 Oct 13.


DOI:10.1080/09273948.2023.2266730
PMID:37831553
Abstract

PURPOSE: To assess the diagnostic performance of two chatbots, ChatGPT and Glass, in uveitis diagnosis compared to renowned uveitis specialists, and evaluate clinicians' perception about utilizing artificial intelligence (AI) in ophthalmology practice. METHODS: Six cases were presented to uveitis experts, ChatGPT (version 3.5 and 4.0) and Glass 1.0, and diagnostic accuracy was analyzed. Additionally, a survey about the emotions, confidence in utilizing AI-based tools, and the likelihood of incorporating such tools in clinical practice was done. RESULTS: Uveitis experts accurately diagnosed all cases (100%), while ChatGPT achieved a diagnostic success rate of 66% and Glass 1.0 achieved 33%. Most attendees felt excited or optimistic about utilizing AI in ophthalmology practice. Older age and high level of education were positively correlated with increased inclination to adopt AI-based tools. CONCLUSIONS: ChatGPT demonstrated promising diagnostic capabilities in uveitis cases and ophthalmologist showed enthusiasm for the integration of AI into clinical practice.

摘要

目的:评估两个聊天机器人 ChatGPT 和 Glass 在葡萄膜炎诊断方面的表现,与著名的葡萄膜炎专家进行比较,并评估临床医生对在眼科实践中使用人工智能 (AI) 的看法。

方法:向葡萄膜炎专家、ChatGPT(版本 3.5 和 4.0)和 Glass 1.0 展示了六个病例,并分析了诊断准确性。此外,还进行了一项关于使用 AI 工具的情绪、信心以及将此类工具纳入临床实践的可能性的调查。

结果:葡萄膜炎专家准确诊断了所有病例(100%),而 ChatGPT 的诊断成功率为 66%,Glass 1.0 为 33%。大多数与会者对在眼科实践中使用 AI 感到兴奋或乐观。年龄较大和教育程度较高与采用基于 AI 的工具的意愿增加呈正相关。

结论:ChatGPT 在葡萄膜炎病例中表现出有希望的诊断能力,眼科医生对将 AI 融入临床实践表现出热情。

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Chatbots Vs. Human Experts: Evaluating Diagnostic Performance of Chatbots in Uveitis and the Perspectives on AI Adoption in Ophthalmology.

Ocul Immunol Inflamm. 2024-10

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Medicine (Baltimore). 2025-8-29

[2]
Application prospect of large language model represented by ChatGPT in ophthalmology.

Int J Ophthalmol. 2025-9-18

[3]
Large language models in ophthalmology: a scoping review on their utility for clinicians, researchers, patients, and educators.

Eye (Lond). 2025-8-25

[4]
Large language models in the management of chronic ocular diseases: a scoping review.

Front Cell Dev Biol. 2025-6-18

[5]
The acceptance of ophthalmic artificial intelligence for eye diseases: a literature review and qualitative analysis.

Eye (Lond). 2025-6-13

[6]
Large Language Models in Medical Diagnostics: Scoping Review With Bibliometric Analysis.

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[7]
Using Large Language Models to Enhance Exercise Recommendations and Physical Activity in Clinical and Healthy Populations: Scoping Review.

JMIR Med Inform. 2025-5-27

[8]
A systematic review and meta-analysis of diagnostic performance comparison between generative AI and physicians.

NPJ Digit Med. 2025-3-22

[9]
Performance of ChatGPT in Ophthalmic Registration and Clinical Diagnosis: Cross-Sectional Study.

J Med Internet Res. 2024-11-14

[10]
Evaluation of the Appropriateness and Readability of ChatGPT-4 Responses to Patient Queries on Uveitis.

Ophthalmol Sci. 2024-8-8

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