ARTIFICIAL INTELLIGENCE IN AGE-RELATED MACULAR DEGENERATION: POTENTIAL CLINICAL IMPLICATION
Abstract
Introduction: Age-related macular degeneration (AMD) constitutes a significant health concern. AMD is associated with vision impairment. A timely diagnosis and personalized therapeutic strategies are key determinants of a favorable disease course. Diagnosis and monitoring of disease progression are mainly based on imaging modalities. Currently, the interpretation of imaging results is performed by clinicians, which is time-consuming, expensive and often limited due to disparities in access to healthcare. The application of artificial intelligence (AI) for image analysis seems to be an innovative approach. This approach will facilitate the clinical management of patients with AMD, who require frequent follow-up visits to monitor disease progression, evaluate therapeutic efficacy, and determine the necessity of therapeutic escalation.
Materials and Methods: This study is a literature review based on recent literature including clinical trials, meta-analyses and randomized controlled trials. The methodology involved a literature search from 2020-2025 across electronic databases, including PubMed, Scopus and Google Scholar, The keywords search terms like “Artificial intelligence and age-related macular degeneration”, “Artificial intelligence and optical coherence tomography”.
Results: AI models present high diagnostic accuracy in AMD, achieving sensitivity and specificity above 90%. AI model results are often comparable or better than clinical assessment. AI effectively detects disease and predicts progression, supporting treatment planning, including anti-VEGF scheduling. Performance may decline across devices and heterogeneous cohorts. Recent innovation, specially multimodal systems including various imaging tests, indicate high diagnostic maturity and clear clinical usefulness.
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