Review demonstrates how artificial intelligence, multi-omics, and digital twins advance individualized therapy across diseases, highlighting clinical translation barriers.
Precision medicine is being reshaped by the convergence of artificial intelligence (AI), multi-omics profiling, and digital twin modelling. This review synthesizes spanning bioinformatics, drug discovery, cardiology, and pharmacogenomics to examine how foundation models, generative networks, and patient-specific virtual replicas are jointly advancing disease interpretation and individualized therapy. We describe representative AI architectures across omics layers, summarize digital twin applications in cardiology and oncology, and outline barriers including data heterogeneity, interpretability, cost, and equity. Coordinated, implementation-focused development is identified as the key determinant of clinical translation.
No takes yet. Share an insight, caveat, or question.
Priyaranjan et al. (2026) studied this question.
Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context: