AI-driven cardiovascular risk prediction in type 2 diabetes shows promise but bias and lack of diversity limit clinical useNew machine learning tools show promise but need better testing for diverse patients with diabetes

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.