Abstract
BackgroundIn 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.MethodsThis 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.ResultsThere 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.ConclusionsThe 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.
Highlights
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
Through AI, researchers can make the preliminary diagnosis of skin cancers, achieve rapid intraoperative diagnosis of brain tumors, diagnose 55 common diseases in pediatrics based on electronic medical records in Chinese, identify rare genetic diseases through facial photographs, and generate the findings that early and frequent patient movements can reduce the risk of postintensive care syndrome and long-term dysfunction after analyzing patients’ movement activities in the intensive care units. [11,12,13,14,15]
The Concerning the attitudes toward AI, both medical workers and other professional technicians were relatively confident in human doctors, with only a very small proportion of people thinking that AI in ophthalmology would completely replace ophthalmologists
Summary
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. Fundus photography and OCT are regular examinations used in ophthalmology, through which a vast amount of high-quality standard images can be obtained. These images are suitable for analysis and process by AI deep learning technology to further assist doctors in diagnosing ophthalmopathies. [16, 17] The researchers from Sun Yat-sen University in China have developed a deep learning model called CC-Cruiser for recognizing congenital cataract, which is able to diagnose blinding diseases such as age-related macular degeneration and diabetic macular edema after trained with deep learning algorithms based on OCT images [18, 19]. There are related studies that use AI technology for the segmentation of ophthalmic images [20, 21], and the classification of ophthalmopathies [22,23,24], etc
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