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

Gunasekeran DV 1 , Zheng F 1 , Lim GYS 1 , Chong CCY 1 , Zhang S 1 , Ng WY 1 Show all authors , Keel S 2 , Xiang Y 3 , Park KH 4 , Park SJ 4 , Chandra A 5 , Wu L 6 , Campbel JP 7 , Lee AY 8 , Keane PA 9 , Denniston A 10 , Lam DSC 11 , Fung AT 12 , Chan PRV 13 , Sadda SR 14 , Loewenstein A 15 , Grzybowski A 16 , Fong KCS 17 , Wu WC 18 , Bachmann LM 19 , Zhang X 3 , Yam JC 20 , Cheung CY 20 , Pongsachareonnont P 21 , Ruamviboonsuk P 22 , Raman R 23 , Sakamoto T 24 , Habash R 25 , Girard M 1 , Milea D 1 , Ang M 1 , Tan GSW 1 , Schmetterer L 1 , Cheng CY 1 , Lamoureux E 1 , Lin H 3 , van Wijngaarden P 2 , Wong TY 1 , Ting DSW 1

Affiliations 

  • 1 Singapore Eye Research Institute (SERI), Singapore National Eye Center (SNEC), Singapore, Singapore
  • 2 Department of Ophthalmology, University of Melbourne, Melbourne, VIC, Australia
  • 3 State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center (ZOC), Sun Yat-sen University, Guangzhou, China
  • 4 Department of Ophthalmology, Seoul National University College of Medicine, Seoul, South Korea
  • 5 Department of Ophthalmology, Southend University Hospital, Southend-on-Sea, United Kingdom
  • 6 Asociados de Macula, Vitreo y Retina de Costa Rica, San José, Costa Rica
  • 7 Casey Eye Institute, Oregon Health and Science, Portland, OR, United States
  • 8 Department of Ophthalmology, University of Washington, Seattle, WA, United States
  • 9 Moorfields Eye Hospital, London, United Kingdom
  • 10 Department of Ophthalmology, University Hospitals Birmingham NHS Foundation Trust, Birmingham, United Kingdom
  • 11 International Eye Research Institute of the Chinese University of Hong Kong (Shenzhen), Shenzhen, China
  • 12 Specialty of Clinical Ophthalmology and Eye Health, Faculty of Medicine and Health, Westmead Clinical School, The University of Sydney, Sydney, NSW, Australia
  • 13 Department of Ophthalmology, University of Illinois College of Medicine, Chicago, IL, United States
  • 14 Department of Ophthalmology, Doheny Eye Institute, Los Angeles, CA, United States
  • 15 Department of Ophthalmology, Tel Aviv Medical Center, Tel Aviv, Israel
  • 16 Department of Ophthalmology, University of Warmia and Mazury, Olsztyn, Poland
  • 17 OasisEye Specialists, Kuala Lumpur, Malaysia
  • 18 Department of Ophthalmology, Chang Gung Memorial Hospital, Taoyuan, Taiwan
  • 19 Oculocare Medical AG, Zurich, Switzerland
  • 20 Department of Ophthalmology and Visual Sciences, The Chinese University of Hong Kong (CUHK), Hong Kong, Hong Kong SAR, China
  • 21 Vitreoretinal Research Unit, Department of Ophthalmology, Chulalongkorn University and King Chulalongkorn Memorial Hospital, Bangkok, Thailand
  • 22 Department of Ophthalmology, College of Medicine, Rangsit University, Rajavithi Hospital, Bangkok, Thailand
  • 23 Vitreo-Retinal Department, Sankara Nethralaya, Chennai, India
  • 24 Department of Ophthalmology, Kagoshima University, Kagoshima, Japan
  • 25 Bascom Palmar Eye Institute, Miami, FL, United States
Front Med (Lausanne), 2022;9:875242.
PMID: 36314006 DOI: 10.3389/fmed.2022.875242

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.

* Title and MeSH Headings from MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine.

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