A panoramic perspective: application prospects and outlook of multimodal artificial intelligence in the management of diabetic retinopathy

Abstract

Diabetic retinopathy (DR) is a leading cause of blindness among the working-age population, and its management is challenged by the disease's inherent heterogeneity. Current management paradigms, based on standardized grading, are inadequate for addressing the significant inter-patient variability in disease progression and treatment response, thereby limiting the implementation of personalized medicine. While artificial intelligence (AI) has achieved breakthroughs in unimodal analysis of retinal images, the single dimension of information fails to capture the complete, complex pathophysiology of DR. Against this backdrop, multimodal AI, capable of integrating heterogeneous data from multiple sources, has garnered widespread attention and is regarded as a revolutionary tool to overcome current bottlenecks and achieve a panoramic understanding for the management of each patient. This review aims to systematically explore the frontier research and developmental potential of multimodal AI in DR management. It focuses on its data sources, core fusion technologies, and application framework across the entire management workflow. Furthermore, this review analyzes future challenges and directions, with the goal of providing a theoretical reference and guidance for the advancement of precision medicine in DR.