Integrating Artificial Intelligence and Multicenter Real-World Evidence to Advance Precision Dosing: A Critical Perspective Review

BACKGROUND: Precision dosing integrates patient-specific factors to optimize pharmacotherapy. However, current evidence often relies on limited single-center studies, restricting generalizability. METHODS: A narrative review of literature on real-world evidence, pharmacogenetics, biomarkers, artificial intelligence (AI), and privacy-preserving data strategies relevant to precision dosing was conducted. RESULTS: Single-center data are insufficient for robust precision-dosing evidence because of small subgroups and local practice biases. Multicenter privacy-preserving systems enable richer, more generalizable datasets that link doses, exposures, genotypes, biomarkers, and outcomes. However, multicenter collaborations should remain aligned with the intended population and should not imply indiscriminate pooling of heterogeneous data. Secure pooled environments, federated learning, and synthetic data play distinct method-dependent roles. AI supports exposure estimation, phenotype extraction, and modeling of complex relationships but requires rigorous validation against conventional methods. Causal inference requires epidemiological rigor beyond predictive performance. CONCLUSIONS: Advancing precision dosing requires multicenter, purpose-specific, privacy-preserving, methodologically rigorous, and clinically validated data systems guided by clinical pharmacologists, with AI serving as a complementary tool rather than a default solution.