AI-driven cardiovascular risk prediction in patients with diabetes: bridging algorithmic innovation to equitable clinical application
Year: 2026
Type: article
Abstract: Machine learning models hold promise to revolutionize cardiovascular disease (CVD) prediction in patients with type 2 diabetes, with algorithms such as neural networks demonstrating superior discriminative performance in internal validations. However, a systematic review has revealed that existing models generally carry a high risk of bias and exhibit poor adherence to transparent reporting standards, severely hindering their clinical translation and real-world application. Furthermore, current models are predominantly developed using populations from Europe and North America, resulting in a critical lack of representativeness for Asian populations, where the burden of cardiovascular disease is particularly heavy. This article argues that the field is undergoing a pivotal transition-from an exclusive focus on algorithmic performance to ensuring clinical equity and fairness. Future advancements should prioritize external validation, calibration-aware assessment, subgroup-specific performance reporting, and cautious integration of biologically plausible biomarkers rather than relying on discrimination alone. Only through this approach can machine learning-driven predictive tools truly bridge the gap between innovation and equitable clinical implementation, ultimately alleviating the global burden of diabetes-related cardiovascular complications.
Source: Frontiers in Medicine
Authors: Hongxuan Li, Zheyi Xu, Yanhui Cen, Xin Liu
Institution: Guangxi University of Chinese Medicine
Language: English
Subfield: Health Information Management
Field: Health Professions
Domain: Health Sciences
Open Access status: gold
Estimated APC paid: $3,801
OpenAlex ID: W7163219793