Public articles linked to the same research event.
World Journal of Methodology This paper assesses why high-performing artificial intelligence systems for ocular image processing seldom translate into improved patient outcomes, locating the central problem in the disparity between pixel-level performance metrics and their clinical significance, naming data bias, domain shift, and label noise alongside the lack of prospective randomized deployment trials as primary obstacles, and outlining a path through stringent external validation, established decision criteria, ongoing surveillance in real clinical practice, transparent reporting standards, and deliberate human-factors engineering, with the goal of converting algorithmic accuracy into meaningful diagnostic precision for glaucoma, diabetic retinopathy, and macular conditions (specifically diabetic macular edema and
This paper assesses why high-performing artificial intelligence systems for ocular image processing seldom translate into improved patient outcomes, locating the central problem in the disparity between pixel-level performance metrics and their clinical significance, naming data bias, domain shift, and label noise alongside the lack of prospective randomized deployment trials as primary obstacles, and outlining a path through stringent external validation, established decision criteria, ongoing surveillance in real clinical practice, transparent reporting standards, and deliberate human-factors engineering, with the goal of converting algorithmic accuracy into meaningful diagnostic precision for glaucoma, diabetic retinopathy, and macular conditions (specifically diabetic macular edema and
This paper assesses why high-performing artificial intelligence systems for ocular image processing seldom translate into improved patient outcomes, locating the central problem in the disparity between pixel-level performance metrics and their clinical significance, naming data bias, domain shift, and label noise alongside the lack of prospective randomized deployment trials as primary obstacles, and outlining a path through stringent external validation, established decision criteria, ongoing surveillance in real clinical practice, transparent reporting standards, and deliberate human-factors engineering, with the goal of converting algorithmic accuracy into meaningful diagnostic precision for glaucoma, diabetic retinopathy, and macular conditions (specifically diabetic macular edema and
This paper assesses why high-performing artificial intelligence systems for ocular image processing seldom translate into improved patient outcomes, locating the central problem in the disparity between pixel-level performance metrics and their clinical significance, naming data bias, domain shift, and label noise alongside the lack of prospective randomized deployment trials as primary obstacles, and outlining a path through stringent external validation, established decision criteria, ongoing surveillance in real clinical practice, transparent reporting standards, and deliberate human-factors engineering, with the goal of converting algorithmic accuracy into meaningful diagnostic precision for glaucoma, diabetic retinopathy, and macular conditions (specifically diabetic macular edema and