Advancing systemic disease diagnosis through ophthalmic image-based artificial intelligence

Abstract

The eye serves as a unique window into systemic health, offering clinicians a valuable opportunity for early detection and targeted treatment. Against this backdrop, advancements in artificial intelligence (AI) and ophthalmic imaging are converging to pave the way for more precise and predictive diagnostics. This review aims to elucidate the transformative role of AI in utilizing ophthalmic imaging for the detection and prediction of systemic diseases. We begin by introducing the advantages of the eye as a valuable tool for detecting systemic diseases. We also provide an overview of various ophthalmic imaging techniques that have proven useful in predicting systemic ailments. Then, we summarize two research patterns for analyzing ocular data, followed by the introduction of current AI applications using ophthalmic images that significantly increase diagnostic precision. Despite the promise, challenges such as data heterogeneity and model interpretability persist, which are also covered in this review. We conclude by discussing future directions and the immense potential these AI-enabled approaches hold for revolutionizing healthcare. As AI technologies advance, their potential integration with ophthalmic imaging offers promising avenues for improving the diagnosis, prediction, and management of various systemic diseases, thereby contributing to the evolving landscape of integrated healthcare.

Key words： artificial intelligence; model interpretability; ophthalmic imaging; systemic diseases