The Use of ChatGPT to Assist in Diagnosing Glaucoma Based on Clinical Case Reports.
The Use of ChatGPT to Assist in Diagnosing Glaucoma Based on Clinical Case Reports.
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DOI：
10.1007/s40123-023-00805-x
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发表时间：
2023-12
影响因子：
3.3
通讯作者：
Yousefi, Siamak
中科院分区：
文献类型：
--
作者：
Delsoz, Mohammad;Raja, Hina;Madadi, Yeganeh;Tang, Anthony A.;Wirostko, Barbara M.;Kahook, Malik Y.;Yousefi, Siamak
关键词：
这项研究的目的是评估大型语言模型，如Chat生成性预训练转换器(ChatGPT)根据特定临床病例描述诊断青光眼的能力，并与高级眼科住院医师实习生的表现进行比较。我们从一个可公开访问的在线病例报告数据库中选择了11例原发性和继发性青光眼患者。其中4例为原发性青光眼，包括开角型青光眼、青少年青光眼、正常眼压性青光眼和闭角型青光眼；继发性青光眼7例，包括假性剥脱、色素弥散性青光眼、青光眼睫状体炎危象、无晶状体眼、新生血管、房水误导和炎症性青光眼。我们将每个病例细节的文本输入到ChatGPT，并要求进行临时诊断和鉴别诊断。然后，我们向三位资深眼科住院医师介绍了11例的详细情况，并记录了他们的临时诊断和鉴别诊断。最后，我们根据正确的诊断和评估的协议对响应进行了评估。11例中有8例(72.7%)的初步诊断正确，3名眼科住院医师的诊断正确分别为6例(54.5%)、8例(72.7%)和8例(72.7%)。ChatGPT与第一、第二和第三眼科住院医师之间的一致性分别为9、7和7。使用特定的案例，ChatGPT在诊断原发性和继发性青光眼患者方面的准确性与资深眼科住院医生相似或更好。随着进一步的发展，ChatGPT可能有潜力用于临床护理环境，如初级保健办公室，用于分类和眼科护理临床实践，为青光眼患者提供客观和快速的诊断。
The purpose of this study was to evaluate the capabilities of large language models such as Chat Generative Pretrained Transformer (ChatGPT) to diagnose glaucoma based on specific clinical case descriptions with comparison to the performance of senior ophthalmology resident trainees. We selected 11 cases with primary and secondary glaucoma from a publicly accessible online database of case reports. A total of four cases had primary glaucoma including open-angle, juvenile, normal-tension, and angle-closure glaucoma, while seven cases had secondary glaucoma including pseudo-exfoliation, pigment dispersion glaucoma, glaucomatocyclitic crisis, aphakic, neovascular, aqueous misdirection, and inflammatory glaucoma. We input the text of each case detail into ChatGPT and asked for provisional and differential diagnoses. We then presented the details of 11 cases to three senior ophthalmology residents and recorded their provisional and differential diagnoses. We finally evaluated the responses based on the correct diagnoses and evaluated agreements. The provisional diagnosis based on ChatGPT was correct in eight out of 11 (72.7%) cases and three ophthalmology residents were correct in six (54.5%), eight (72.7%), and eight (72.7%) cases, respectively. The agreement between ChatGPT and the first, second, and third ophthalmology residents were 9, 7, and 7, respectively. The accuracy of ChatGPT in diagnosing patients with primary and secondary glaucoma, using specific case examples, was similar or better than senior ophthalmology residents. With further development, ChatGPT may have the potential to be used in clinical care settings, such as primary care offices, for triaging and in eye care clinical practices to provide objective and quick diagnoses of patients with glaucoma.
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