Digital Twins in Healthcare, Industry, and Infrastructure: Scoping Review with ☸️SAIMSARA.

Name: SAIMSARA Evidence Object digital::DIGITAL_TWINS_PM
Creator: SAIMSARA
License: https://saimsara.com/license/

SAIMSARA

doi:10.62487/saimsara7e35ff6e

SAIMSARA Journal

Machine-Readable Science • ISSN 3054-3991

Digital Twins in Healthcare, Industry, and Infrastructure: Scoping Review with ☸️SAIMSARA.

Digital Health & Biotech

Issue 3, Volume 1, 2026

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DOI: 10.62487/saimsara7e35ff6e

Editorial note

• Last update: 2026-05-25 22:07:10

What is this paper about

Digital twins are moving beyond engineering hype into practical decision systems for medicine, industry, infrastructure, and public health—linking real-world data to virtual models that can predict, optimize, and guide action. This full evidence map of 220 references and 1,347 original studies shows where digital twins already deliver measurable signals, where they remain experimental, and which domains are closest to real-world clinical or operational impact.

Human-verified editorial review Verified by World ID proof-of-human. This editorial layer was submitted from a SAIMSARA account verified as a unique human.

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Abstract: To map original research on “digital twin” applications and synthesize the principal study-level findings, with emphasis on recurrent evidence signals, implementation domains, real-world decision-support functions, and future research needs. The review uses 220 references and builds its evidence map from 1347 original studies with 23,830,156 total participants/sample observations (topic-deduplicated ΣN). Across heterogeneous domains, the evidence indicates that digital twins are converging on a shared role as continuously updated, model-based decision systems that link physical or biological observations to virtual simulations for prediction, monitoring, and intervention planning. This dominant signal was most concrete in clinical personalization, such as glioma radiotherapy optimization yielding a 16.7% median dose reduction with a 6-day gain in tumor time to progression, and in operational settings where coal-mine ventilation twins achieved 97.3% accuracy with 27% energy reduction. Procedural guidance with submillimetric to millimetric spatial accuracy further supports a role for digital twins in real-time decision support. However, the predominance of experimental and single-center studies highlights uncertainty about real-world clinical and operational benefit. Future work should prioritize prospective comparative trials with patient- and system-centered endpoints to establish where digital twin deployment delivers durable, generalizable value.

Keywords: Digital twin; Cyber-physical systems; Real-time monitoring; Simulation; Machine learning; Predictive modeling; Internet of Things; Decision support; Process optimization; Personalized medicine

Review Stats

Final search date and database lock: 2026-05-18 20:48:49 CEST
Plan: Pro (expanded craft tokens; source: PubMed)
Source: PubMed
Total Abstracts/Papers: 2704
Downloaded Abstracts/Papers: 2704
Included original and non-original Abstracts/Papers (all): 1691
Included original Abstracts/Papers (Vote counting by direction of effect): 1347
Reference Index (links used in paper): 220
Total participants/sample observations (topic deduplicated ΣN): 23,830,156

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Reference Index (220)

[1] Twin-S: a digital twin for skull base surgery. — https://doi.org/10.1007/s11548-023-02863-9
[2] Digital twin rehabilitation system based on self-balancing lower limb exoskeleton. — https://doi.org/10.3233/thc-220087
[3] Digital Twin of an Optical Measurement System. — https://doi.org/10.3390/s21196638
[4] Blockchain-secure patient Digital Twin in healthcare using smart contracts. — https://doi.org/10.1371/journal.pone.0286120
[5] Digital-twin imaging based on descattering Gaussian splatting. — https://doi.org/10.1364/oe.564226
[6] AI Based Digital Twin Model for Cattle Caring. — https://doi.org/10.3390/s22197118
[7] Digital Twin Prospects in IoT-Based Human Movement Monitoring Model. — https://doi.org/10.3390/s25216674
[8] Digital Twin Platform for Water Treatment Plants Using Microservices Architecture. — https://doi.org/10.3390/s24051568

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