Artificial Intelligence in Cataract Care: A Narrative Review of Evidence Maturity and Clinical Readiness

Artificial intelligence (AI) is being investigated across the cataract care pathway, but the maturity of the evidence varies substantially between technical benchmark studies, externally validated models, prospective clinical evaluations and tools used in routine care. This narrative review critically summarises AI applications in cataract detection, intraocular lens (IOL) power calculation, perioperative risk assessment, intraoperative support and postoperative follow‐up, with particular emphasis on evidence quality, external validity and clinical readiness. A structured search of PubMed/MEDLINE was conducted for English‐language publications from 1 January 2018 through 31 March 2025, with selective retention of earlier seminal studies relevant to cataract risk prediction, force feedback and ophthalmic robotics. Evidence was classified by maturity, from technical proof of concept and retrospective internal validation to external or multicentre validation, prospective clinical evaluation and demonstrated workflow or patient‐level benefit. The strongest evidence for cataract detection comes from large externally validated image‐based models, whereas smartphone systems remain sensitive to image quality, case mix and deployment setting. Data‐driven IOL formulas can improve refractive prediction in selected datasets, but comparisons are affected by constant optimisation, axial‐length distribution, IOL model, measurement device and paired‐eye dependence. Intraoperative AI remains dominated by phase recognition, segmentation and technical performance assessment, with limited evidence of direct clinical benefit. Postoperative conversational AI has reached multicentre prospective clinical evaluation, although safety depends on escalation pathways, human oversight and local workflow design. Clinical translation of AI in cataract care is therefore emerging but uneven: a limited number of applications have reached external or prospective clinical validation, while many remain retrospective or technical proof‐of‐concept systems. Future adoption should be based on external validation, transparent reporting, prospective clinical utility, postdeployment monitoring and clear regulatory and medicolegal accountability rather than benchmark performance alone.