TY - JOUR

T1 - Acceptance and Perception of Artificial Intelligence Usability in Eye Care (APPRAISE) for Ophthalmologists

T2 - A Multinational Perspective

AU - Gunasekeran, Dinesh V.

AU - Zheng, Feihui

AU - Lim, Gilbert Y. S.

AU - Chong, Crystal C. Y.

AU - Zhang, Shihao

AU - Ng, Wei Yan

AU - Keel, Stuart

AU - Xiang, Yifan

AU - Park, Ki Ho

AU - Park, Sang Jun

AU - Chandra, Aman

AU - Wu, Lihteh

AU - Campbel, J. Peter

AU - Lee, Aaron Y.

AU - Keane, Pearse A.

AU - Denniston, Alastair

AU - Lam, Dennis S. C.

AU - Fung, Adrian T.

AU - Chan, Paul R. V.

AU - Sadda, SriniVas R.

AU - Loewenstein, Anat

AU - Grzybowski, Andrzej

AU - Fong, Kenneth C. S.

AU - Wu, Wei-chi

AU - Bachmann, Lucas M.

AU - Zhang, Xiulan

AU - Yam, Jason C.

AU - Cheung, Carol Y.

AU - Pongsachareonnont, Pear

AU - Ruamviboonsuk, Paisan

AU - Raman, Rajiv

AU - Sakamoto, Taiji

AU - Habash, Ranya

AU - Girard, Michael

AU - Milea, Dan

AU - Ang, Marcus

AU - Tan, Gavin S. W.

AU - Schmetterer, Leopold

AU - Cheng, Ching-Yu

AU - Lamoureux, Ecosse

AU - Lin, Haotian

AU - van Wijngaarden, Peter

AU - Wong, Tien Y.

AU - Ting, Daniel S. W.

N1 - Publisher Copyright: Copyright © 2022 Gunasekeran, Zheng, Lim, Chong, Zhang, Ng, Keel, Xiang, Park, Park, Chandra, Wu, Campbel, Lee, Keane, Denniston, Lam, Fung, Chan, Sadda, Loewenstein, Grzybowski, Fong, Wu, Bachmann, Zhang, Yam, Cheung, Pongsachareonnont, Ruamviboonsuk, Raman, Sakamoto, Habash, Girard, Milea, Ang, Tan, Schmetterer, Cheng, Lamoureux, Lin, van Wijngaarden, Wong and Ting.

PY - 2022

Y1 - 2022

N2 - Background: Many artificial intelligence (AI) studies have focused on development of AI models, novel techniques, and reporting guidelines. However, little is understood about clinicians' perspectives of AI applications in medical fields including ophthalmology, particularly in light of recent regulatory guidelines. The aim for this study was to evaluate the perspectives of ophthalmologists regarding AI in 4 major eye conditions: diabetic retinopathy (DR), glaucoma, age-related macular degeneration (AMD) and cataract. Methods: This was a multi-national survey of ophthalmologists between March 1st, 2020 to February 29th, 2021 disseminated via the major global ophthalmology societies. The survey was designed based on microsystem, mesosystem and macrosystem questions, and the software as a medical device (SaMD) regulatory framework chaired by the Food and Drug Administration (FDA). Factors associated with AI adoption for ophthalmology analyzed with multivariable logistic regression random forest machine learning. Results: One thousand one hundred seventy-six ophthalmologists from 70 countries participated with a response rate ranging from 78.8 to 85.8% per question. Ophthalmologists were more willing to use AI as clinical assistive tools (88.1%, n = 890/1,010) especially those with over 20 years' experience (OR 3.70, 95% CI: 1.10–12.5, p = 0.035), as compared to clinical decision support tools (78.8%, n = 796/1,010) or diagnostic tools (64.5%, n = 651). A majority of Ophthalmologists felt that AI is most relevant to DR (78.2%), followed by glaucoma (70.7%), AMD (66.8%), and cataract (51.4%) detection. Many participants were confident their roles will not be replaced (68.2%, n = 632/927), and felt COVID-19 catalyzed willingness to adopt AI (80.9%, n = 750/927). Common barriers to implementation include medical liability from errors (72.5%, n = 672/927) whereas enablers include improving access (94.5%, n = 876/927). Machine learning modeling predicted acceptance from participant demographics with moderate to high accuracy, and area under the receiver operating curves of 0.63–0.83. Conclusion: Ophthalmologists are receptive to adopting AI as assistive tools for DR, glaucoma, and AMD. Furthermore, ML is a useful method that can be applied to evaluate predictive factors on clinical qualitative questionnaires. This study outlines actionable insights for future research and facilitation interventions to drive adoption and operationalization of AI tools for Ophthalmology.

AB - Background: Many artificial intelligence (AI) studies have focused on development of AI models, novel techniques, and reporting guidelines. However, little is understood about clinicians' perspectives of AI applications in medical fields including ophthalmology, particularly in light of recent regulatory guidelines. The aim for this study was to evaluate the perspectives of ophthalmologists regarding AI in 4 major eye conditions: diabetic retinopathy (DR), glaucoma, age-related macular degeneration (AMD) and cataract. Methods: This was a multi-national survey of ophthalmologists between March 1st, 2020 to February 29th, 2021 disseminated via the major global ophthalmology societies. The survey was designed based on microsystem, mesosystem and macrosystem questions, and the software as a medical device (SaMD) regulatory framework chaired by the Food and Drug Administration (FDA). Factors associated with AI adoption for ophthalmology analyzed with multivariable logistic regression random forest machine learning. Results: One thousand one hundred seventy-six ophthalmologists from 70 countries participated with a response rate ranging from 78.8 to 85.8% per question. Ophthalmologists were more willing to use AI as clinical assistive tools (88.1%, n = 890/1,010) especially those with over 20 years' experience (OR 3.70, 95% CI: 1.10–12.5, p = 0.035), as compared to clinical decision support tools (78.8%, n = 796/1,010) or diagnostic tools (64.5%, n = 651). A majority of Ophthalmologists felt that AI is most relevant to DR (78.2%), followed by glaucoma (70.7%), AMD (66.8%), and cataract (51.4%) detection. Many participants were confident their roles will not be replaced (68.2%, n = 632/927), and felt COVID-19 catalyzed willingness to adopt AI (80.9%, n = 750/927). Common barriers to implementation include medical liability from errors (72.5%, n = 672/927) whereas enablers include improving access (94.5%, n = 876/927). Machine learning modeling predicted acceptance from participant demographics with moderate to high accuracy, and area under the receiver operating curves of 0.63–0.83. Conclusion: Ophthalmologists are receptive to adopting AI as assistive tools for DR, glaucoma, and AMD. Furthermore, ML is a useful method that can be applied to evaluate predictive factors on clinical qualitative questionnaires. This study outlines actionable insights for future research and facilitation interventions to drive adoption and operationalization of AI tools for Ophthalmology.

KW - artificial intelligence (AI)

KW - implementation

KW - ophthalmology

KW - regulation

KW - translation

U2 - 10.3389/fmed.2022.875242

DO - 10.3389/fmed.2022.875242

M3 - Journal article

C2 - 36314006

AN - SCOPUS:85140588682

VL - 9

JO - Frontiers in Medicine

JF - Frontiers in Medicine

SN - 2296-858X

M1 - 875242

ER -