Accuracy and Effectiveness of AI-Powered Systems in Patient Counseling, Education, and Management in Optometry and Related Eye-Care Settings: A Systematic Review.

Background/Objectives: Artificial intelligence (AI), including large language models, chatbots, machine-learning systems, and hybrid tools, is increasingly used to support patient-facing eye-care communication. This systematic review evaluated the accuracy and effectiveness of AI-powered systems used for patient counseling, education, communication, referral/follow-up, and management support in optometry and related eye-care settings. Methods: A systematic review was conducted according to a predefined protocol and PRISMA 2020 reporting principles. Searches covered studies published from January 2020 to 31 May 2026 in PubMed, Embase, Web of Science, Cochrane Library, and IEEE Xplore. Eligible studies were original primary studies evaluating AI-supported tools for patient-facing counseling, education, question answering, treatment or medication guidance, triage, referral, follow-up, screening linked to management, or clinical decision support. Methodological quality was appraised using relevant JBI critical appraisal tools. Results: Thirty-nine studies were included in the qualitative synthesis. JBI appraisal indicated heterogeneous study designs and reporting quality, with common limitations related to simulated prompts or AI-output evaluations, variable comparators, and inconsistent outcome reporting. Evidence was dominated by patient-facing large language models and chatbot-based tools used for patient education, question answering, readability improvement, glaucoma and myopia counseling, diabetic retinopathy referral/follow-up, cataract education, oculoplastic and retinal-condition questions, and multilingual educational support. Study designs, AI models, prompting approaches, comparators, clinical topics, and outcome definitions varied widely, supporting narrative synthesis rather than quantitative pooling. Conclusions: AI-powered systems show potential as supervised adjunctive tools for eye-care counseling, education, and management-related communication. However, evidence remains heterogeneous and dependent on simulated prompts, AI-generated outputs, and model-based evaluations. Future research should prioritize standardized evaluation, real-patient validation, safety monitoring, readability control, and patient-centered outcomes before routine implementation in optometry and broader eye-care practice across diverse clinical settings.