Explainable artificial intelligence in ophthalmology

Abstract

Purpose of review Despite the growing scope of artificial intelligence (AI) and deep learning (DL) applications in the field of ophthalmology, most have yet to reach clinical adoption. Beyond model performance metrics, there has been an increasing emphasis on the need for explainability of proposed DL models. Recent findings Several explainable AI (XAI) methods have been proposed, and increasingly applied in ophthalmological DL applications, predominantly in medical imaging analysis tasks. Summary We summarize an overview of the key concepts, and categorize some examples of commonly employed XAI methods. Specific to ophthalmology, we explore XAI from a clinical perspective, in enhancing end-user trust, assisting clinical management, and uncovering new insights. We finally discuss its limitations and future directions to strengthen XAI for application to clinical practice.

Novel artificial intelligence algorithms for diabetic retinopathy and diabetic macular edema

“…This has implications in establishing the robustness of AI models with data from different sources, and also culpability in the case of incorrect predictions. As such, explainable AI (XAI) has understandably become an important consideration in AI model acceptance [ 82 ].…”