Aspiration to architecture: multi-omics, AI, digital twins, and blockchain for P4 medicine
Two decades after its formalization, P4 medicine (predictive, preventive, personalized, participatory) remains more framework than practice. Most implementations stall at single-omics prediction and fail to close the loop across all four dimensions. In this Perspective, we argue that the P4 framework becomes actionable only when each pillar is anchored to a specific, implementable digital technology: multi-omics for prediction, artificial intelligence for prevention, digital twinning for personalization, and blockchain for participation. We propose a tiered multi-omics classification (Tier 1: genomics, measured once; Tier 2: epigenomics/proteomics, periodic; Tier 3: metabolomics/wearables, frequent) and present preliminary metabolomic aging data from 2,072 individuals identifying nine metabolites with linear age associations. We offer a computational definition of personalization requiring baseline state estimation, trajectory prediction, and counterfactual intervention simulation via stochastic digital twin engines. For the participatory pillar, we describe a blockchain architecture enabling patient-controlled data sovereignty and a health data marketplace. These four technologies form a reinforcing flywheel, where longitudinal patient participation enriches upstream data layers. We discuss validation challenges, regulatory gaps, equity concerns, and privacy risks that must be addressed before clinical deployment.
Authors
- Paniz Jasbi (ORCID: https://orcid.org/0000-0002-2129-3098)
- Alex E. Mohr (ORCID: https://orcid.org/0000-0001-5401-3702)
- Thomas E. Ichim (ORCID: https://orcid.org/0000-0001-5611-374X)
- Anil Bajnath (ORCID: https://orcid.org/0009-0006-5684-4933)
- Gail King
Institutions
- American Board of Internal Medicine (US)
- American Board of Emergency Medicine (US)
- Precision Research (United States) (US)
- Biota Pharmaceuticals (United States) (US)
Publication Details
- Journal
- Journal of Translational Medicine
- Published
- 2026-05-29
- DOI
- https://doi.org/10.1186/s12967-026-08346-0
- Primary Topic
- Artificial Intelligence in Healthcare and Education
- Type
- article
- Field-Weighted Citation Impact
- 0.00