Artificial intelligence in ophthalmology: From diagnostic accuracy to clinical application

Artificial intelligence in ophthalmology encounters a continual challenge: Systems proficient in picture classi- fication seldom yield quantifiable enhancements in patient outcomes. The primary concern is the disparity between pixel-level performance metrics and their clinical significance. Primary obstacles encompass data bias, domain shift, and label noise, exacerbated by the lack of prospective, randomized deployment trials. The frequent dis- regard for patient-centered objectives, cost-effectiveness, and equity evaluations is significant. Rectifying these deficiencies necessitates stringent external validation, established decision criteria, and ongoing surveillance within actual clinical practices. Transparent reporting criteria and the deliberate incorporation of human-factors engi- neering are essential. Only by bridging this gap can algorithmic accuracy be converted into significant diagnostic precision for glaucoma, diabetic retinopathy, and macular conditions (specifically diabetic macular edema and age- related macular degeneration). This paper aims to assess the limits of using high-performing artificial intelligence systems in ocular image processing, which seldom lead to enhanced patient outcomes, and to delineate the sci- entific, clinical, and practical techniques required to close this gap.