ReviewFrontiers in digital health2026
Multi-scale digital twins for personalized medicine.
Review in Frontiers in digital health, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
4 citing papers in PubMed.
- Clinical trials in a dish: cardiometabolic drug development with biological and digital twins.The Journal of clinical investigation · 2026Review
- From digital twins to clinically trustworthy twins: a clinical-claim-based validation framework for personalized digital health.Frontiers in digital health · 2026Article
- Multilayer network approaches to omics data integration in digital twins for cancer research.Frontiers in systems biology · 2026Review
- Artificial Intelligence as a Disruptive Force in Pharmaceutical Innovation: Transforming Discovery, Development, and Manufacturing.Drug design, development and therapy · 2026Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
1 author.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The future of personalized medicine requires moving beyond isolated data streams toward integrated, multi-scale representations of human health. Digital twins (DTs) have emerged as promising solutions, offering dynamic, individualized simulations of biological systems. However, current implementations often rely on narrow data sources, limiting predictive power, adaptability, and clinical utility. The next generation of digital twins must integrate molecular, cellular, tissue, organ, clinical, behavioral, and environmental data to accurately model health trajectories and disease evolution. This review synthesizes the conceptual foundations, technical architectures, clinical applications, and ethical challenges associated with multi-scale digital twins (MSDTs). Key enabling technologies include multimodal data fusion, graph neural networks, causal inference frameworks, reinforcement learning, and hybrid mechanistic-AI modeling approaches. Clinical applications can illustrate the potential of MSDTs to personalize interventions dynamically. Significant barriers persist regarding data integration, ethical governance, bias mitigation, and regulatory adaptation.
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Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.