Diabetes mellitus (DM) predisposes patients to vascular complications.
Retinal images and vasculature reflect the body's micro- and macrovascular
health. They can be used to diagnose DM complications, including diabetic
retinopathy (DR), neuropathy, nephropathy, and atherosclerotic cardiovascular
disease, as well as forecast the risk of cardiovascular events. Artificial
intelligence (AI)-enabled systems developed for high-throughput detection of DR
using digitized retinal images have become clinically adopted. Beyond DR
screening, AI integration also holds immense potential to address challenges
associated with the holistic care of the patient with DM. In this work, we aim
to comprehensively review the literature for studies on AI applications based
on retinal images related to DM diagnosis, prognostication, and management. We
will describe the findings of holistic AI-assisted diabetes care, including but
not limited to DR screening, and discuss barriers to implementing such systems,
including issues concerning ethics, data privacy, equitable access, and
explainability. With the ability to evaluate the patient's health status vis a
vis DM complication as well as risk prognostication of future cardiovascular
complications, AI-assisted retinal image analysis has the potential to become a
central tool for modern personalized medicine in patients with DM.

通过综合评估与糖尿病病症相关的视网膜图像、风险预测并探讨伦理、数据隐私等问题，基于人工智能技术的视网膜图像分析有潜力成为糖尿病患者现代个性化医学的核心工具。