Arinzechukwu Nkemdirim Okere

Advancing stroke prevention in atrial fibrillation: a systematic review of machine learning-based risk prediction models

In light of the pervasive methodological limitations identified, including high analytic risk of bias, absence of external validation, and lack of model interpretability, claims of ML superiority over CHA2DS2-VASc must be interpreted with caution.