Attitudes of medical workers in China toward artificial intelligence in ophthalmology: a comparative survey

Abstract

Background In the development of artificial intelligence in ophthalmology, the ophthalmic AI-related recognition issues are prominent, but there is a lack of research into people’s familiarity with and their attitudes toward ophthalmic AI. This survey aims to assess medical workers’ and other professional technicians’ familiarity with, attitudes toward, and concerns about AI in ophthalmology. Methods This is a cross-sectional study design study. An electronic questionnaire was designed through the app Questionnaire Star, and was sent to respondents through WeChat, China’s version of Facebook or WhatsApp. The participation was voluntary and anonymous. The questionnaire consisted of four parts, namely the respondents’ background, their basic understanding of AI, their attitudes toward AI, and their concerns about AI. A total of 562 respondents were counted, with 562 valid questionnaires returned. The results of the questionnaires are displayed in an Excel 2003 form. Results There were 291 medical workers and 271 other professional technicians completed the questionnaire. About 1/3 of the respondents understood AI and ophthalmic AI. The percentages of people who understood ophthalmic AI among medical workers and other professional technicians were about 42.6 % and 15.6 %, respectively. About 66.0 % of the respondents thought that AI in ophthalmology would partly replace doctors, about 59.07 % having a relatively high acceptance level of ophthalmic AI. Meanwhile, among those with AI in ophthalmology application experiences (30.6 %), above 70 % of respondents held a full acceptance attitude toward AI in ophthalmology. The respondents expressed medical ethics concerns about AI in ophthalmology. And among the respondents who understood AI in ophthalmology, almost all the people said that there was a need to increase the study of medical ethics issues in the ophthalmic AI field. Conclusions The survey results revealed that the medical workers had a higher understanding level of AI in ophthalmology than other professional technicians, making it necessary to popularize ophthalmic AI education among other professional technicians. Most of the respondents did not have any experience in ophthalmic AI but generally had a relatively high acceptance level of AI in ophthalmology, and there was a need to strengthen research into medical ethics issues.

Acceptance of clinical artificial intelligence among physicians and medical students: A systematic review with cross-sectional survey

“…Thirty-five included studies mentioned the knowledge level of physicians or medical students on clinical AI, of which 26 (74.29%) showed that participants lacked basic knowledge ( 16 – 19 , 23 , 25 , 26 , 28 – 46 ). Many physicians felt that the current training and educational tools, provided by their departments, were inadequate ( 47 , 48 ).…”

“…Forty-five included studies mentioned the views of physicians and medical students on clinical AI, and more than 60% of the respondents in 38 (84.44%) studies had an optimistic outlook regarding it ( 11 , 12 , 17 – 20 , 22 – 26 , 29 , 30 , 32 , 33 , 35 , 36 , 38 – 41 , 45 – 61 ). For example, 75% of 487 pathologists from 59 countries were enthusiastic about the progress of AI ( 11 ); 77% of 1271 dermatologists from 92 countries agreed that AI would improve dermatologic practice ( 19 ).…”

“…Similar positive opinions also existed among radiologists ( 22 , 23 , 25 , 29 , 33 , 39 , 47 , 48 , 53 , 55 , 61 ), gastroenterologists ( 24 , 35 ), general practitioners ( 28 , 62 ), psychiatrists ( 59 , 63 ), ophthalmologists ( 23 , 50 ). Additionally, in 14 studies reporting use intentionality, more than 60% respondents in 10 (71.43%) studies were willing to incorporate AI into their clinical practice ( 17 , 21 , 26 , 34 , 36 , 44 , 49 , 55 , 56 , 61 ). The perceived benefits of AI included promoting workflow efficiency, quality assurance, improving standardization in the interpretation of results, as well as liberating doctors from mundane tasks and providing more time to expand their medical knowledge and focus on interacting with patients ( 11 , 22 , 35 , 50 , 64 ).…”

Development and Application of an Intelligent Diagnosis System for Retinal Vein Occlusion Based on Deep Learning

“…As suggested in the results, the intelligent diagnosis was less sensitive in groups 1 to 3 than in group 0, indicating further optimization and investigation of the diagnostic model. In future studies, the samples for model [24]. We expect more cooperation between ophthalmologists and experts in the field of artificial intelligence in attempt to define a more concise network model to reduce operating costs.…”

Diabetic Retinopathy Grading by Deep Graph Correlation Network on Retinal Images Without Manual Annotations

“…Fundus images with professional interpretation are a commonly accepted screening strategy for blindness prevention, with superior capability for in-person dilated eye examinations ( 5 ). However, the procedures for observing diabetic retinopathy confront challenges by factors correlated to execution, availability of professionals, and substantial financial sustainability ( 6 – 9 ). The automatic grading of diabetic retinopathy appears to have potential advantages in solving these obstacles, such as increasing efficiency, scalability, and coverage of examining procedures, extending applications in developed regions, and improving patient prevention by providing early diagnosis and referral.…”