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. 2022 May 1;11(3):287-293.
doi: 10.1097/APO.0000000000000525.

Patients Perceptions of Artificial Intelligence in Diabetic Eye Screening

Aaron Yap  1 , Benjamin Wilkinson  2 , Eileen Chen  3 , Lydia Han  3 , Ehsan Vaghefi  3   4 , Chris Galloway  5 , David Squirrell  1   4
Affiliations
Free article

Patients Perceptions of Artificial Intelligence in Diabetic Eye Screening

Aaron Yap et al. Asia Pac J Ophthalmol (Phila). .
Free article

Abstract

Purpose: Artificial intelligence (AI) technology is poised to revolutionize modern delivery of health care services. We set to evaluate the patient perspective of AI use in diabetic retinal screening.

Design: Survey.

Methods: Four hundred thirty-eight patients undergoing diabetic retinal screening across New Zealand participated in a survey about their opinion of AI technology in retinal screening. The survey consisted of 13 questions covering topics of awareness, trust, and receptivity toward AI systems.

Results: The mean age was 59 years. The majority of participants identified as New Zealand European (50%), followed by Asian (31%), Pacific Islander (10%), and Maori (5%). Whilst 73% of participants were aware of AI, only 58% have heard of it being implemented in health care. Overall, 78% of respondents were comfortable with AI use in their care, with 53% saying they would trust an AI-assisted screening program as much as a health professional. Despite having a higher awareness of AI, younger participants had lower trust in AI systems. A higher proportion of Maori and Pacific participants indicated a preference toward human-led screening. The main perceived benefits of AI included faster diagnostic speeds and greater accuracy.

Conclusions: There is low awareness of clinical AI applications among our participants. Despite this, most are receptive toward the implementation of AI in diabetic eye screening. Overall, there was a strong preference toward continual involvement of clinicians in the screening process. There are key recommendations to enhance the receptivity of the public toward incorporation of AI into retinal screening programs.

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Conflict of interest statement

The authors have no other or conflicts of interest to declare.

References

    1. Ramessur R, Raja L, Kilduff CLS, et al. Impact and challenges of integrating artificial intelligence and telemedicine into clinical ophthalmology. Asia Pac J Ophthalmol (Phila) 2021;10:317–327.
    1. Esteva A, Kuprel B, Novoa RA, et al. Dermatologist-level classification of skin cancer with deep neural networks. Nature 2017;542:115–118.
    1. Wong DCS, Kiew G, Jeon S, et al. Singapore Eye Lesions Analyzer (SELENA): the deep learning system for retinal diseases. Artificial Intelligence in Ophthalmology 2021;Springer, 177–185.
    1. Zhelev Z, Peters J, Rogers M, et al. Automated Grading to Replace Level 1 Graders in the Diabetic Eye Screening Programme. 2021;UK National Screening Committee,
    1. Vaghefi E, Yang S, Xie L, et al. THEIA™ development, and testing of artificial intelligence-based primary triage of diabetic retinopathy screening images in New Zealand. Diabet Med 2021;38:e14386.