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What can GPT-4 do for Diagnosing Rare Eye Diseases? A Pilot Study

INTRODUCTION: Generative pretrained transformer-4 (GPT-4) has gained widespread attention from society, and its potential has been extensively evaluated in many areas. However, investigation of GPT-4’s use in medicine, especially in the ophthalmology field, is still limited. This study aims to evalu...

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Autores principales: Hu, Xiaoyan, Ran, An Ran, Nguyen, Truong X., Szeto, Simon, Yam, Jason C., Chan, Carmen K. M., Cheung, Carol Y.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer Healthcare 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10640532/
https://www.ncbi.nlm.nih.gov/pubmed/37656399
http://dx.doi.org/10.1007/s40123-023-00789-8
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author Hu, Xiaoyan
Ran, An Ran
Nguyen, Truong X.
Szeto, Simon
Yam, Jason C.
Chan, Carmen K. M.
Cheung, Carol Y.
author_facet Hu, Xiaoyan
Ran, An Ran
Nguyen, Truong X.
Szeto, Simon
Yam, Jason C.
Chan, Carmen K. M.
Cheung, Carol Y.
author_sort Hu, Xiaoyan
collection PubMed
description INTRODUCTION: Generative pretrained transformer-4 (GPT-4) has gained widespread attention from society, and its potential has been extensively evaluated in many areas. However, investigation of GPT-4’s use in medicine, especially in the ophthalmology field, is still limited. This study aims to evaluate GPT-4’s capability to identify rare ophthalmic diseases in three simulated scenarios for different end-users, including patients, family physicians, and junior ophthalmologists. METHODS: We selected ten treatable rare ophthalmic disease cases from the publicly available EyeRounds service. We gradually increased the amount of information fed into GPT-4 to simulate the scenarios of patient, family physician, and junior ophthalmologist using GPT-4. GPT-4’s responses were evaluated from two aspects: suitability (appropriate or inappropriate) and accuracy (right or wrong) by senior ophthalmologists (> 10 years’ experiences). RESULTS: Among the 30 responses, 83.3% were considered "appropriate" by senior ophthalmologists. In the scenarios of simulated patient, family physician, and junior ophthalmologist, seven (70%), ten (100%), and eight (80%) responses were graded as “appropriate” by senior ophthalmologists. However, compared to the ground truth, GPT-4 could only output several possible diseases generally without “right” responses in the simulated patient scenarios. In contrast, in the simulated family physician scenario, 50% of GPT-4's responses were “right,” and in the simulated junior ophthalmologist scenario, the model achieved a higher “right” rate of 90%. CONCLUSION: To our knowledge, this is the first proof-of-concept study that evaluates GPT-4’s capacity to identify rare eye diseases in simulated scenarios involving patients, family physicians, and junior ophthalmologists. The results indicate that GPT-4 has the potential to serve as a consultation assisting tool for patients and family physicians to receive referral suggestions and an assisting tool for junior ophthalmologists to diagnose rare eye diseases. However, it is important to approach GPT-4 with caution and acknowledge the need for verification and careful referrals in clinical settings.
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spelling pubmed-106405322023-11-15 What can GPT-4 do for Diagnosing Rare Eye Diseases? A Pilot Study Hu, Xiaoyan Ran, An Ran Nguyen, Truong X. Szeto, Simon Yam, Jason C. Chan, Carmen K. M. Cheung, Carol Y. Ophthalmol Ther Brief Report INTRODUCTION: Generative pretrained transformer-4 (GPT-4) has gained widespread attention from society, and its potential has been extensively evaluated in many areas. However, investigation of GPT-4’s use in medicine, especially in the ophthalmology field, is still limited. This study aims to evaluate GPT-4’s capability to identify rare ophthalmic diseases in three simulated scenarios for different end-users, including patients, family physicians, and junior ophthalmologists. METHODS: We selected ten treatable rare ophthalmic disease cases from the publicly available EyeRounds service. We gradually increased the amount of information fed into GPT-4 to simulate the scenarios of patient, family physician, and junior ophthalmologist using GPT-4. GPT-4’s responses were evaluated from two aspects: suitability (appropriate or inappropriate) and accuracy (right or wrong) by senior ophthalmologists (> 10 years’ experiences). RESULTS: Among the 30 responses, 83.3% were considered "appropriate" by senior ophthalmologists. In the scenarios of simulated patient, family physician, and junior ophthalmologist, seven (70%), ten (100%), and eight (80%) responses were graded as “appropriate” by senior ophthalmologists. However, compared to the ground truth, GPT-4 could only output several possible diseases generally without “right” responses in the simulated patient scenarios. In contrast, in the simulated family physician scenario, 50% of GPT-4's responses were “right,” and in the simulated junior ophthalmologist scenario, the model achieved a higher “right” rate of 90%. CONCLUSION: To our knowledge, this is the first proof-of-concept study that evaluates GPT-4’s capacity to identify rare eye diseases in simulated scenarios involving patients, family physicians, and junior ophthalmologists. The results indicate that GPT-4 has the potential to serve as a consultation assisting tool for patients and family physicians to receive referral suggestions and an assisting tool for junior ophthalmologists to diagnose rare eye diseases. However, it is important to approach GPT-4 with caution and acknowledge the need for verification and careful referrals in clinical settings. Springer Healthcare 2023-09-01 2023-12 /pmc/articles/PMC10640532/ /pubmed/37656399 http://dx.doi.org/10.1007/s40123-023-00789-8 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 Brief Report
Hu, Xiaoyan
Ran, An Ran
Nguyen, Truong X.
Szeto, Simon
Yam, Jason C.
Chan, Carmen K. M.
Cheung, Carol Y.
What can GPT-4 do for Diagnosing Rare Eye Diseases? A Pilot Study
title What can GPT-4 do for Diagnosing Rare Eye Diseases? A Pilot Study
title_full What can GPT-4 do for Diagnosing Rare Eye Diseases? A Pilot Study
title_fullStr What can GPT-4 do for Diagnosing Rare Eye Diseases? A Pilot Study
title_full_unstemmed What can GPT-4 do for Diagnosing Rare Eye Diseases? A Pilot Study
title_short What can GPT-4 do for Diagnosing Rare Eye Diseases? A Pilot Study
title_sort what can gpt-4 do for diagnosing rare eye diseases? a pilot study
topic Brief Report
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10640532/
https://www.ncbi.nlm.nih.gov/pubmed/37656399
http://dx.doi.org/10.1007/s40123-023-00789-8
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