Large language models and their impact in ophthalmology

被引：75

作者：

Betzler, Bjorn Kaijun ^{[3
]}

Chen, Haichao ^{[4
]}

Cheng, Ching -Yu ^{[1
,2
,5
,6
]}

Lee, Cecilia S. ^{[7
]}

Ning, Guochen ^{[8
]}

Song, Su Jeong ^{[9
]}

Lee, Aaron Y. ^{[7
]}

Kawasaki, Ryo ^{[10
,11
]}

van Wijngaarden, Peter ^{[12
,13
]}

Grzybowski, Andrzej ^{[14
]}

He, Mingguang ^{[15
]}

Li, Dawei ^{[16
]}

Ran, An Ran ^{[17
]}

Ting, Daniel Shu Wei ^{[5
,6
]}

Teo, Kelvin ^{[5
]}

Ruamviboonsuk, Paisan ^{[18
]}

Sivaprasad, Sobha ^{[19
]}

Chaudhary, Varun ^{[20
]}

Tadayoni, Ramin ^{[21
,22
]}

Wang, Xiaofei ^{[23
]}

Cheung, Carol Y. ^{[17
]}

Zheng, Yingfeng ^{[24
,25
]}

Wang, Ya Xing ^{[26
]}

Tham, Yih Chung ^{[1
,2
,5
,6
,27
]}

Wong, Tien Yin ^{[4
,5
]}

机构：

[1] Natl Univ Singapore, Ctr Innovat & Precis Eye Hlth, Singapore, Singapore

[2] Natl Univ Singapore, Dept Ophthalmol, Singapore, Singapore

[3] Natl Univ Singapore, Yong Loo Lin Sch Med, Singapore, Singapore

[4] Tsinghua Univ, Inst Precis Med, Beijing, Peoples R China

[5] Singapore Natl Eye Ctr, Singapore Eye Res Inst, Singapore, Singapore

[6] Duke NUS Med Sch, Ophthalmol & Visual Sci Acad Clin Program, Singapore, Singapore

[7] Univ Washington, Sch Med, Dept Ophthalmol, Seattle, WA USA

[8] Tsinghua Univ, Sch Med, Dept Biomed Engn, Beijing, Peoples R China

[9] Sungkyunkwan Univ, Sch Med, Kangbuk Samsung Hosp, Dept Ophthalmol, Seoul, South Korea

[10] Osaka Univ, Grad Sch Med, Dept Social Med, Div Publ Hlth, Osaka, Japan

[11] Osaka Univ Hosp, Artificial Intelligence Ctr Med Res & Applicat, Osaka, Japan

[12] Univ Melbourne, Ctr Eye Res Australia, Royal Victorian Eye & Ear Hosp, Melbourne, VA, Australia

[13] Univ Melbourne, Dept Surg, Ophthalmol, Melbourne, VA, Australia

[14] Fdn Ophthalmol Dev, Inst Res Ophthalmol, Poznan, Poland

[15] Hong Kong Polytech Univ, Hong Kong, Peoples R China

[16] Peking Univ, Coll Future Technol, Beijing, Peoples R China

[17] Chinese Univ Hong Kong, Dept Ophthalmol & Visual Sci, Hong Kong, Peoples R China

[18] Rajavithi Hosp, Dept Ophthalmol, Bangkok, Thailand

[19] Moorfields Eye Hosp, NIHR Biomed Res Ctr Ophthalmol, London, England

[20] McMaster Univ, Dept Surg, Hamilton, ON, Canada

[21] Univ Paris Cite, Lariboisiere, AP HP, St Louis, France

[22] Rothschild Fdn Hosp, Paris, France

[23] Beihang Univ, Beijing Adv Innovat Ctr Biomed Engn, Sch Biol Sci & Med Engn, Key Lab Biomech & Mechanobiol,Minist Educ, Beijing, Peoples R China

[24] Sun Yat Sen Univ, Zhongshan Ophthalm Ctr, State Key Lab Ophthalmol, Guangzhou, Peoples R China

[25] Guangdong Lab, Guangzhou Regenerat Med & Hlth, Guangzhou, Peoples R China

[26] Capital Med Univ, Beijing Inst Ophthalmol, Beijing Tongren Hosp, Beijing Ophthalmol & Visual Sci Key Lab, Beijing, Peoples R China

[27] Natl Univ Singapore, Yong Loo Lin Sch Med, Dept Ophthalmol, Singapore, Singapore

来源：

LANCET DIGITAL HEALTH | 2023年 / 5卷 / 12期

基金：

英国医学研究理事会;

关键词：

DEEP LEARNING-SYSTEM; DIABETIC-RETINOPATHY; VALIDATION; EFFICACY; IMAGES; CARE;

D O I：

10.1016/S2589-7500(23)00201-7

中图分类号：

R-058 [];

学科分类号：

摘要：

The advent of generative artificial intelligence and large language models has ushered in transformative applications within medicine. Specifically in ophthalmology, large language models offer unique opportunities to revolutionise digital eye care, address clinical workflow inefficiencies, and enhance patient experiences across diverse global eye care landscapes. Yet alongside these prospects lie tangible and ethical challenges, encompassing data privacy, security, and the intricacies of embedding large language models into clinical routines. This Viewpoint highlights the promising applications of large language models in ophthalmology, while weighing up the practical and ethical barriers towards their real-world implementation. This Viewpoint seeks to stimulate broader discourse on the potential of large language models in ophthalmology and to galvanise both clinicians and researchers into tackling the prevailing challenges and optimising the benefits of large language models while curtailing the associated risks.

引用

页码：E917 / E924

页数：8

共 57 条

[1] Will ChatGPT transform healthcare? [J].

不详 .

NATURE MEDICINE, 2023, 29 (03) :505-506

[2]

[Anonymous], 2022, BLOOM 176B PARAMETER, DOI DOI 10.48550/ARXIV.2211.05100

[3] Evaluating the Performance of ChatGPT in Ophthalmology [J].

Antaki, Fares ;

Touma, Samir ;

Milad, Daniel ;

El -Khoury, Jonathan ;

Duval, Renaud .

OPHTHALMOLOGY SCIENCE, 2023, 3 (04)

[4]

Argil, 2023, How to generate ChatGPT images using Argil plugin

[5] Time Requirements of Paper-Based Clinical Workflows and After-Hours Documentation in a Multispecialty Academic Ophthalmology Practice [J].

Baxter, Sally L. ;

Gali, Helena E. ;

Huang, Abigail E. ;

Millen, Marlene ;

El-Kareh, Robert ;

Nudleman, Eric ;

Robbins, Shira L. ;

Heichel, Christopher W. D. ;

Camp, Andrew S. ;

Korn, Bobby S. ;

Kikkawa, Jeffrey E. Lee Don ;

Longhurst, Christopher A. ;

Chiang, Michael F. ;

Hribar, Michelle R. ;

Ohno-Machado, Lucila .

AMERICAN JOURNAL OF OPHTHALMOLOGY, 2019, 206 :161-167

[6] Automated Grading of Age-Related Macular Degeneration From Color Fundus Images Using Deep Convolutional Neural Networks [J].

Burlina, Philippe M. ;

Joshi, Neil ;

Pekala, Michael ;

Pacheco, Katia D. ;

Freund, David E. ;

Bressler, Neil M. .

JAMA OPHTHALMOLOGY, 2017, 135 (11) :1170-1176

[7] Performance of Generative Large Language Models on Ophthalmology Board-Style Questions [J].

Cai, Louis Z. ;

Shaheen, Abdulla ;

Jin, Andrew ;

Fukui, Riya ;

Yi, Jonathan S. ;

Yannuzzi, Nicolas ;

Alabiad, Chrisfouad .

AMERICAN JOURNAL OF OPHTHALMOLOGY, 2023, 254 :141-149

[8] Epidemiology of Eye-Related Emergency Department Visits [J].

Channa, Roomasa ;

Zafar, Syed Nabeel ;

Canner, Joseph K. ;

Haring, R. Sterling ;

Schneider, Eric B. ;

Friedman, David S. .

JAMA OPHTHALMOLOGY, 2016, 134 (03) :312-319

[9] Preoperative Medical Testing and Falls in Medicare Beneficiaries Awaiting Cataract Surgery [J].

Chen, Catherine L. ;

McLeod, Stephen D. ;

Lietman, Thomas M. ;

Shen, Hui ;

Boscardin, W. John ;

Chang, Han-Ying Peggy ;

Whooley, Mary A. ;

Gelb, Adrian W. ;

Lee, Sei J. ;

Dudley, R. Adams .

OPHTHALMOLOGY, 2021, 128 (02) :208-215

[10]

Chen M., 2021, Evaluating large language models trained on code, DOI DOI 10.48550/ARXIV.2107.03374

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