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–\(8\%\) of ILP for small- to medium-scale workloads. HybridQeGA offers the closest match to ILP in larger problem sizes, with less than \(3\%\) deviation in overall objective value while maintaining scalability. Security-sensitive scenarios highlight HybridQeGA’s adaptability, improving security scores by an average of \(12\%\) 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.
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Data availability
No external datasets were generated or analyzed during the current study, as the evaluation was conducted using simulation-based experiments. The implementation details, algorithmic configurations, and parameter settings used in this work are described in the manuscript. Source code and additional simulation materials can be made available upon reasonable request to the corresponding author.
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Acknowledgements
The authors extend their appreciation to Prince Sattam bin Abdulaziz University for funding this research work through the project number PSAU/2025/31124.
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The authors extend their appreciation to Prince Sattam bin Abdulaziz University for funding this research work through the project number (PSAU/2025/31124).
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F. M .A and S. A, created the paper’s fundamental idea. T. A, and S. A, have developed the methodology. S. A, and M. A have analyzed the paper. Original draft was prepared by S. A, which was edited by A. M. A., F. M. A, and S. A . T. A, have supervised the overall work. All authors reviewed the manuscript.
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Alotaibi, F.M., Ahmad, S., Akram, T. et al. A cost-optimized medical digital twin framework for secure and efficient patient data management in smart healthcare. Sci Rep (2026). https://doi.org/10.1038/s41598-026-41205-y
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DOI: https://doi.org/10.1038/s41598-026-41205-y
