Acceptance and Perception of Artificial Intelligence Usability in Eye Care (APPRAISE) for Ophthalmologists: A Multinational Perspective

Dinesh V. Gunasekeran, Feihui Zheng, Gilbert Y. S. Lim, Crystal C. Y. Chong, Shihao Zhang, Wei Yan Ng, Stuart Keel, Yifan Xiang, Ki Ho Park, Sang Jun Park, Aman Chandra, Lihteh Wu, J. Peter Campbel, Aaron Y. Lee, Pearse A. Keane, Alastair Denniston, Dennis S. C. Lam, Adrian T. Fung, Paul R. V. Chan, SriniVas R. SaddaAnat Loewenstein, Andrzej Grzybowski, Kenneth C. S. Fong, Wei-chi Wu, Lucas M. Bachmann, Xiulan Zhang, Jason C. Yam, Carol Y. Cheung, Pear Pongsachareonnont, Paisan Ruamviboonsuk, Rajiv Raman, Taiji Sakamoto, Ranya Habash, Michael Girard, Dan Milea, Marcus Ang, Gavin S. W. Tan, Leopold Schmetterer, Ching-Yu Cheng, Ecosse Lamoureux, Haotian Lin, Peter van Wijngaarden, Tien Y. Wong, Daniel S. W. Ting*

*Corresponding author af dette arbejde

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Abstract

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.

OriginalsprogEngelsk
Artikelnummer875242
TidsskriftFrontiers in Medicine
Vol/bind9
Antal sider19
ISSN2296-858X
DOI
StatusUdgivet - 2022

Bibliografisk note

Funding Information:
We would like to acknowledge the following ophthalmology societies for supporting the dissemination of the survey questionnaires to the society members: American Ophthalmology Society, Asia-Pacific Ocular Imaging Society (APOIS), Singapore College of Ophthalmology, Asia-Pacific Academy of Ophthalmology (APAO); Asia-Pacific Myopia Society (APMS); Asia-Pacific Vitreo-retina Society (APVRS), British and Eire Association of Vitreoretinal Surgeons (BEAVRS), China Ophthalmology Society, Chinese American Association of Ophthalmology, European Academy of Ophthalmology, European Association for Vision and Eye Research, Glaucoma Research Society, The Hong Kong Ophthalmological Society, International Retinal Imaging Symposium (IntRIS), Israel Ophthalmology Society, Japanese Vitreo-retinal Society, Korean Ophthalmology Society, Malaysian Society of Ophthalmology, Pan American Association of Ophthalmology, Switzerland Ophthalmology Society, The Royal Australian and New Zealand College of Ophthalmologists (RANZCO), The Royal College of Ophthalmologists of Thailand, Vitreoretinal Society of India (VRSI), All India Ophthalmological Society (AIOS).

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.

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