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.

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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
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article

Aspiration to architecture: multi-omics, AI, digital twins, and blockchain for P4 medicine

Paniz Jasbi, Alex E. Mohr, Thomas E. Ichim, Anil Bajnath et al.
Journal of Translational Medicine
Artificial Intelligence in Healthcare and Education
article

Aspiration to architecture: multi-omics, AI, digital twins, and blockchain for P4 medicine

Paniz Jasbi, Alex E. Mohr, Thomas E. Ichim, Anil Bajnath, Gail King
article en

Abstract

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.

Journal of Translational Medicine
American Board of Internal Medicine (US), American Board of Emergency Medicine (US), Precision Research (United States) (US), Biota Pharmaceuticals (United States) (US)
Openalex Percentile: Top 8%
Artificial Intelligence in Healthcare and Education
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