The Clinical Significance of Imaging Biomarkers Discoverable by Anomaly Detection Methods in Retinal Diseases: A Review

Abstract

Introduction The increasing prevalence of vision‐threatening retinal diseases poses a growing challenge on healthcare systems worldwide, underscoring the need for effective strategies in disease diagnosis and management. Unsupervised artificial intelligence (AI) approaches, such as anomaly detection, offer the potential to identify established and novel retinal imaging biomarkers. They reduce reliance on expert‐driven annotations, mitigate bias, and overcome the limitations of predefined disease categories. Moreover, when integrated into clinical workflows, they have the potential to enhance practical applicability by supporting efficient screening, longitudinal disease monitoring, and objective assessment of treatment response. Areas Covered This review examines the role of anomaly detection for discovering clinically relevant retinal imaging biomarkers, by categorizing biomarkers according to their underlying pathophysiology, summarizing the spectrum of anomaly detection methods applied in the field, and highlighting biomarkers that have been identified through these approaches. Critical Appraisal Current anomaly detection methods favor the identification of established biomarkers characterized by strong intensity contrasts and well‐defined structural boundaries, while more subtle abnormalities remain difficult to capture. Future research should prioritize integrating large language models (LLMs), foundation methods, multimodality, and few‐shot anomaly detection, and increase explainability to advance the clinical application of anomaly detection for retinal imaging biomarker discovery in ophthalmology.