Warfarin remains one of the most widely used anticoagulants; however, its narrow therapeutic index means that even small dosing deviations can result in thromboembolic or bleeding events, necessitating close monitoring and strict control of the international normalized ratio (INR). Although traditional warfarin dosing algorithms incorporating CYP2C9 and VKORC1 genotypes improve upon fixed-dose regimens, they explain less than 50% of dose variability and perform inconsistently across populations. These limitations underscore the need for more adaptive and precise dosing methodologies. Artificial intelligence (AI) and machine learning (ML) have been recognized as powerful approaches to advance warfarin dose individualization. This narrative review synthesizes literature on machine learning approaches to warfarin dosing, including support vector regression, neural networks, ensemble models, and reinforcement learning, with a focus on predictive performance and clinical relevance. Overall, the literature indicates that ML-based warfarin dosing models may improve prediction of the therapeutic warfarin dose and regulation of INR levels compared with traditional clinical and pharmacogenetic interventions. However, many published models are constrained by small sample sizes and limited external validation, reducing generalizability. Methodological heterogeneity and inconsistent reporting further underscore persistent gaps in the evidence base. AI and ML approaches have shown potential advantages over clinical and pharmacogenetic dosing methods for warfarin, with some studies reporting lower prediction errors and improved therapeutic INR control. However, further studies are needed to draw definitive conclusions about their comparative effectiveness.
Shamohammadi et al. (Fri,) studied this question.