ReviewFrontiers in digital health2026
Human digital twins in personalized and predictive healthcare: a comprehensive review of technologies, applications, and future directions.
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 1 paper.
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
1 citing paper in PubMed.
- Physiological Data Integration and Predictive Modeling in Intensive Care.Life (Basel, Switzerland) · 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
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The role of real-time data, artificial intelligence, and computational modeling is discussed in this review analytics Human Digital Twins (HDTs) creation- virtual persons of personalities patients which advocate predictive simulation to forecast of physiological behavior, treatment responses, and disease tracks. A synthesis of existing knowledge is done up to the technologies is a foundation to HDTs, clinical application and implementation issues of interest to precision medicine. The conceptual basis of engineering of the digital twins is analyzed and production principles, and technologies, which allow to produce HDTs-machine. Are physiological modeling, learning and distributed cloud-based computing infrastructure identified and evaluated. Cards: cardiology, oncology, genomics and immunology are critically appraised. It is based on the comparative analysis of 35 peer-reviewed documents and technical as it was reported, HDTs have great potential in enhancing personalized prediction of side effects, optimization of clinical trial design using virtual, and scheduling of treatment cohort simulation. But, model standards, an important component of model validation, are not present interoperability, ethical governing mechanisms and regulatory avenues to clinical deployment. The main priority research directions are determined, such as the development of common-validation techniques; implementation of federated learning frameworks to support sharing of data with data privacy limitations; incorporation of multi-omics data into physiological models; and introducing open ethical review procedures. This review provides substantive evidence basis to researchers, clinicians and policy makers to market the. Knowledge about HDTs technology to population health and health care provision revolutionizes.
Indexed as
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.