1.Introduction Artificial intelligence(AI)has demonstrated remarkable advancements in ophthalmology,particularly in disease detection,grading,classification,and prediction with equal or even superior performance to ex...1.Introduction Artificial intelligence(AI)has demonstrated remarkable advancements in ophthalmology,particularly in disease detection,grading,classification,and prediction with equal or even superior performance to experienced ophthalmologists.1,2 These findings highlight the promising potential of AI in clinical application and its role as a powerful assistant for ophthalmologists.However,while the clinical utility of AI for ophthalmologists has been well documented,its application in pre-clinic settings to assist patients with eye diseases remains largely unexplored.Few studies evaluated the effectiveness of AI in providing eye health education to patients,which is vital,especially for patients with systemic diseases to prevent or attenuate ocular complications.Vision loss from diabetic retinopathy is largely preventable,but less than two-thirds of diabetic patients adhere to recommended annual eye examination,3 suggesting a huge insufficiency in vision self-management during diabetes course.By investigating and comparing the effectiveness of different AI tools in generating diabetic retinopathy guidelines,this study aimed to offer guidance in selecting AI platforms best suited to promote understanding and adherence to medical guidelines for vision protection.展开更多
基金supported by the National Natural Science Foundation Project of China(82271078)the Project of Youth Beijing Scholar(No.076)Beijing Municipal Public Welfare Development and Reform Pilot Project for Medical Research Institutes(PWD&RPP-MRI,JYY2023-6).
文摘1.Introduction Artificial intelligence(AI)has demonstrated remarkable advancements in ophthalmology,particularly in disease detection,grading,classification,and prediction with equal or even superior performance to experienced ophthalmologists.1,2 These findings highlight the promising potential of AI in clinical application and its role as a powerful assistant for ophthalmologists.However,while the clinical utility of AI for ophthalmologists has been well documented,its application in pre-clinic settings to assist patients with eye diseases remains largely unexplored.Few studies evaluated the effectiveness of AI in providing eye health education to patients,which is vital,especially for patients with systemic diseases to prevent or attenuate ocular complications.Vision loss from diabetic retinopathy is largely preventable,but less than two-thirds of diabetic patients adhere to recommended annual eye examination,3 suggesting a huge insufficiency in vision self-management during diabetes course.By investigating and comparing the effectiveness of different AI tools in generating diabetic retinopathy guidelines,this study aimed to offer guidance in selecting AI platforms best suited to promote understanding and adherence to medical guidelines for vision protection.
