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The Use of ChatGPT to Assist in Diagnosing Glaucoma Based on Clinical Case Reports

INTRODUCTION: 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. M...

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Autores principales: Delsoz, Mohammad, Raja, Hina, Madadi, Yeganeh, Tang, Anthony A., Wirostko, Barbara M., Kahook, Malik Y., Yousefi, Siamak
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer Healthcare 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10640454/
https://www.ncbi.nlm.nih.gov/pubmed/37707707
http://dx.doi.org/10.1007/s40123-023-00805-x
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author Delsoz, Mohammad
Raja, Hina
Madadi, Yeganeh
Tang, Anthony A.
Wirostko, Barbara M.
Kahook, Malik Y.
Yousefi, Siamak
author_facet Delsoz, Mohammad
Raja, Hina
Madadi, Yeganeh
Tang, Anthony A.
Wirostko, Barbara M.
Kahook, Malik Y.
Yousefi, Siamak
author_sort Delsoz, Mohammad
collection PubMed
description INTRODUCTION: 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. METHODS: 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. RESULTS: 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. CONCLUSIONS: 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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spelling pubmed-106404542023-11-15 The Use of ChatGPT to Assist in Diagnosing Glaucoma Based on Clinical Case Reports Delsoz, Mohammad Raja, Hina Madadi, Yeganeh Tang, Anthony A. Wirostko, Barbara M. Kahook, Malik Y. Yousefi, Siamak Ophthalmol Ther Original Research INTRODUCTION: 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. METHODS: 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. RESULTS: 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. CONCLUSIONS: 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. Springer Healthcare 2023-09-14 2023-12 /pmc/articles/PMC10640454/ /pubmed/37707707 http://dx.doi.org/10.1007/s40123-023-00805-x Text en © The Author(s) 2023 https://creativecommons.org/licenses/by-nc/4.0/Open Access This article is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License, which permits any non-commercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc/4.0/ (https://creativecommons.org/licenses/by-nc/4.0/) .
spellingShingle Original Research
Delsoz, Mohammad
Raja, Hina
Madadi, Yeganeh
Tang, Anthony A.
Wirostko, Barbara M.
Kahook, Malik Y.
Yousefi, Siamak
The Use of ChatGPT to Assist in Diagnosing Glaucoma Based on Clinical Case Reports
title The Use of ChatGPT to Assist in Diagnosing Glaucoma Based on Clinical Case Reports
title_full The Use of ChatGPT to Assist in Diagnosing Glaucoma Based on Clinical Case Reports
title_fullStr The Use of ChatGPT to Assist in Diagnosing Glaucoma Based on Clinical Case Reports
title_full_unstemmed The Use of ChatGPT to Assist in Diagnosing Glaucoma Based on Clinical Case Reports
title_short The Use of ChatGPT to Assist in Diagnosing Glaucoma Based on Clinical Case Reports
title_sort use of chatgpt to assist in diagnosing glaucoma based on clinical case reports
topic Original Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10640454/
https://www.ncbi.nlm.nih.gov/pubmed/37707707
http://dx.doi.org/10.1007/s40123-023-00805-x
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