人工智能驱动的糖尿病并发症预测：从临床数据到分子组学

摘要(中文译文)

糖尿病并发症是导致患者残疾和死亡的主要原因，早期识别高风险个体对于精准预防和管理至关重要。近年来，人工智能（AI）的快速发展为糖尿病护理中的风险预测和临床决策支持提供了变革性工具。在这篇叙述性综述中，我们系统调查了2015年1月至2025年6月期间发表于PubMed、Web of Science和Scopus的研究，这些研究将基于AI的预测建模应用于三种主要糖尿病并发症：糖尿病视网膜病变（DR）、糖尿病肾病（DN）和糖尿病心血管疾病（CVD）。共纳入58项研究，涵盖基于临床特征、分子组学、医学影像和多模态数据整合的模型。跨尺度与多模态数据融合已成为一种有前景的新范式，在三种主要糖尿病并发症中均展现出优于单模态方法的预测性能。我们还总结了从传统机器学习到深度学习、以及近来向大语言模型和基于智能体的系统的演进，比较了它们的方法学特征、优势及适用应用场景。最后，我们提出了一个可操作的六步框架及AI在糖尿病并发症中的临床转化路径，概述了从数据整理、模型开发到验证、法规合规和真实世界实施的关键步骤。综上，这些见解为开发稳健、透明且可临床部署的AI系统提供了路线图，有望变革糖尿病并发症的预防和管理。

Abstract

Diabetic complications are a major cause of disability and mortality among patients, and early identification of high-risk individuals is essential for precision prevention and management. In recent years, the rapid advancement of artificial intelligence (AI) has provided transformative tools for risk prediction and clinical decision support in diabetes care. In this narrative review, we systematically surveyed studies published between January 2015 and June 2025 in PubMed, Web of Science, and Scopus that applied AI-based predictive modeling for three major diabetic complications: diabetic retinopathy (DR), diabetic nephropathy (DN), and diabetic cardiovascular disease (CVD). A total of 58 studies were included, encompassing models based on clinical features, molecular omics, medical imaging, and multimodal data integration. Cross-scale and multimodal data fusion has emerged as a promising new paradigm, demonstrating improved predictive performance over single-modality approaches in three major diabetic complications. We also summarize the evolution from traditional machine learning to deep learning and, more recently, to large language models and agent-based systems, comparing their methodological characteristics, strengths, and suitable application scenarios. Finally, we proposed an actionable six-step framework and clinical translation pathway for AI in diabetic complications, outlining key steps from data curation and model development to validation, regulatory compliance, and real-world implementation. Together, these insights provide a roadmap toward developing robust, transparent, and clinically deployable AI systems capable of transforming the prevention and management of diabetic complications.

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