ArticleScientific reports2026
A cost-optimized medical digital twin framework for secure and efficient patient data management in smart healthcare.
Article in Scientific reports, 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.
- Narrative Review of Digital Twins in the Health Domain: Development, Application, and Evidence Consolidation.Medical sciences (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
6 authors.
Funding
Abstract
The increasing demand for personalized, real-time healthcare necessitates efficient, secure patient data management. Digital Twins (DTs) enable AI-powered monitoring and decision support but also introduce challenges related to latency, computational cost, and security. This paper proposes a cost-optimized, AI-driven Medical Digital Twin (MDT) framework that manages task allocation across heterogeneous edge, fog, and cloud infrastructures. The system is formulated as a tri-objective optimization model that jointly minimizes latency and operational cost while maximizing security, subject to resource and clinical-priority constraints. To solve this problem, three complementary approaches are developed: (i) an exact Integer Linear Programming (ILP) model for optimal benchmarking, (ii) a Patient-Aware Task Intelligence Greedy (PATI-Greedy) heuristic algorithm for low-latency decision-making, and (iii) a Hybrid Q-Learning Enhanced Genetic Algorithm (HybridQeGA) for scalable, near-optimal performance in complex environments. Extensive simulations in a smart ICU scenario with 4, 8, and 12 patients demonstrate that ILP consistently achieves the best objective values but is computationally impractical for large instances. PATI-Greedy executes rapidly with polynomial complexity, achieving results within 5-[Formula: see text] of ILP for small- to medium-scale workloads. HybridQeGA offers the closest match to ILP in larger problem sizes, with less than [Formula: see text] deviation in overall objective value while maintaining scalability. Security-sensitive scenarios highlight HybridQeGA's adaptability, improving security scores by an average of [Formula: see text] compared to PATI-Greedy. These findings establish a balanced trade-off between accuracy and computational efficiency, positioning the proposed framework as a robust and deployable solution for intelligent and trustworthy digital health ecosystems.
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.