AI-driven cardiovascular risk prediction in patients with diabetes: bridging algorithmic innovation to equitable clinical application

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Machine learning models show promise in predicting cardiovascular disease in type 2 diabetes patients, outperforming traditional risk assessment tools.
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Existing models often exhibit high bias and poor adherence to reporting standards, limiting their clinical applicability and real-world use.
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Current algorithms are predominantly developed using European and North American populations, lacking representativeness for Asian populations.
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Future advancements should prioritize external validation, subgroup-specific performance, and integration of biomarkers for equitable clinical implementation.
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The shift in focus from algorithmic performance to clinical fairness is essential for bridging the gap between innovation and real-world utility.

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