Patients Perceptions of Artificial Intelligence in Diabetic Eye Screening

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 Ma ¯ori (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.

Use of artificial intelligence on retinal images to accurately predict the risk of cardiovascular event (CVD-AI)

“…Previously we have demonstrated that it is possible to train an artificial intelligence (AI) deep learning (DL) algorithm on retinal images to grade diabetic retinopathy and maculopathy for diagnostic, screening and risk assessment purposes [30,[36][37][38][39][40][41][42][43][44][45]. In this study we used 110,272 fundus images from a database of 55,118 patients from the UK Biobank and AREDS 1 datasets to train and subsequently test a novel AI platform (CVD-AI) to calculate a 10-year CVD risk score for these individuals.…”

Artificial Intelligence in Eye Disease: Recent Developments, Applications, and Surveys

“…In other words, DL architectures are used for learning to recognize a variety of eye-related diseases in ophthalmology to improve diagnosis rates with clinically acceptable performance, compared to ophthalmology specialists [ 6 ]. Thus, AI could effectively serve as a reliable safety platform for both patients and doctors, and as an auxiliary tool to promptly judge the results; this could not only reduce the possibility of misdiagnosis, but could also improve patient experience by expediting efficient treatment [ 7 ].…”

Patients Perceptions of Artificial Intelligence in a Deep Learning-Assisted Diabetic Retinopathy Screening Event: A Real-World Assessment

“…Understanding patients' attitudes is crucial for the successful integration of artificial intelligence (AI) in healthcare, but patients' perceptions on this matter have been seldom studied. [1][2][3][4] As AI is currently applied for diabetic retinopathy (DR) screening, our objective was to evaluate patients' perceptions on AI during a DR screening event.…”

“…Nevertheless, by collecting answers on-site, during an actual (as opposed to hypothetical) situation, and reporting patients' perspectives on AI, we believe our research brings robust and original information and helps shedding light onto this very important but often neglected stakeholder. [1][2][3] The answers were given in the realm of a specific application, as opposed to a general approach of AI in health. 1 While only 14% reported good or expert knowledge in AI, most had positive views of AI in health but prefer a cooperation of AI and human physicians, in line with other reports.…”

“…While only 14% reported good or expert knowledge in AI, most had positive views of AI in health but prefer a cooperation of AI and human physicians, in line with other reports. 1-3…”