From Simulation to Implementation of the Impact of Digital Twins on Healthcare System
Article Sidebar
Main Article Content
Abstract
Digital Twin (DT) technology has emerged as a new healthcare framework that enables medical professionals to create virtual models of patients, clinical settings, and entire health systems through real-time data processing. Digital twin technology began as an engineering and manufacturing tool that industrial organisations developed using Internet of Things (IoT), artificial intelligence (AI), machine learning (ML), and cloud-edge computing technologies. The review examines how healthcare digital twins have shifted from theoretical simulation tools to active, real-time systems that help doctors make treatment decisions while improving system efficiency. The research begins by identifying the fundamental elements of healthcare digital twins and their technological foundations. The research investigates four main application areas: patient-centric models for personalised care; predictive analytics and operational twins to optimise hospital resources and manage workflows; digital twins for device monitoring and predictive maintenance; and population-level twins for public health surveillance and epidemic response. The review examines key implementation obstacles, including data privacy problems, technical interoperability issues, ethical dilemmas, and the need for regulatory and standardisation systems. The review provides researchers, clinicians, and policymakers with essential resources to understand how digital twin technologies will change future healthcare systems.
Article Details
Section
This work is licensed under a Creative Commons Attribution 4.0 International License.
How to Cite
References
[1] K. Zhang, H.-Y. Zhou, D.T. Baptista-Hon, Y. Gao, X. Liu, E. Oermann, S. Xu, S. Jin, J. Zhang, Z. Sun, others, Concepts and applications of digital twins in healthcare and medicine, Patterns 5 (2024).
[2] M. Ringeval, F.A. Etindele Sosso, M. Cousineau, G. Paré, Advancing health care with digital twins: meta-review of applications and implementation challenges, J. Med. Internet Res. 27 (2025) e69544.
[3] A. Vallée, Digital twin for healthcare systems, Front. Digit. Health 5 (2023) 1253050.
[4] M. Nadeem, S. Kostic, M. Dornhöfer, C. Weber, M. Fathi, A comprehensive review of digital twin in healthcare in the scope of simulative health-monitoring, Digit. Health 11 (2025) 20552076241304080.
[5] R. Kaul, C. Ossai, A.R.M. Forkan, P.P. Jayaraman, J. Zelcer, S. Vaughan, N. Wickramasinghe, The role of AI for developing digital twins in healthcare: The case of cancer care, Wiley Interdiscip. Rev. Data Min. Knowl. Discov. 13 (2023) e1480.
[6] C. Meijer, H.-W. Uh, S. El Bouhaddani, Digital twins in healthcare: Methodological challenges and opportunities, J. Pers. Med. 13 (2023) 1522.
[7] V. Riahi, I. Diouf, S. Khanna, J. Boyle, H. Hassanzadeh, Digital Twins for Clinical and Operational Decision-Making: Scoping Review, J. Med. Internet Res. 27 (2025) e55015.
[8] D. Drummond, A. Gonsard, Definitions and characteristics of patient digital twins being developed for clinical use: scoping review, J. Med. Internet Res. 26 (2024) e58504.
[9] B.H. Tudor, R. Shargo, G.M. Gray, J.L. Fierstein, F.H. Kuo, R. Burton, J.T. Johnson, B.B. Scully, A. Asante-Korang, M.A. Rehman, others, A scoping review of human digital twins in healthcare applications and usage patterns, NPJ Digit. Med. 8 (2025) 587.
[10] X. Li, J. Loscalzo, A.K.M.F. Mahmud, D.M. Aly, A. Rzhetsky, M. Zitnik, M. Benson, Digital twins as global learning health and disease models for preventive and personalized medicine, Genome Med. 17 (2025) 11.
[11] E. Katsoulakis, Q. Wang, H. Wu, L. Shahriyari, R. Fletcher, J. Liu, L. Achenie, H. Liu, P. Jackson, Y. Xiao, others, Digital twins for health: a scoping review, NPJ Digit. Med. 7 (2024) 77.
[12] G.D.K. Kuruppu Appuhamilage, M. Hussain, M. Zaman, W. Ali Khan, A health digital twin framework for discrete event simulation based optimised critical care workflows, NPJ Digit. Med. 8 (2025) 376.
[13] B.R. Barricelli, E. Casiraghi, D. Fogli, A survey on digital twin: Definitions, characteristics, applications, and design implications, IEEE Access 7 (2019) 167653–167671.
[14] K. Papachristou, P.F. Katsakiori, P. Papadimitroulas, L. Strigari, G.C. Kagadis, Digital twins’ advancements and applications in healthcare, towards precision medicine, J. Pers. Med. 14 (2024) 1101.
[15] C. Meijer, H.-W. Uh, S. El Bouhaddani, Digital twins in healthcare: Methodological challenges and opportunities, J. Pers. Med. 13 (2023) 1522.
[16] M.R. Kabir, F.S. Shishir, S. Shomaji, S. Ray, Digital twins in healthcare IoT: A systematic review, High-Confidence Computing (2025) 100340.
[17] S.Y. Saratkar, M. Langote, P. Kumar, P. Gote, I.N. Weerarathna, G. Mishra, Digital twin for personalized medicine development, Front. Digit. Health 7 (2025) 1583466.
[18] M. Strocchi, D.J. Hammersley, B.P. Halliday, S.K. Prasad, S.A. Niederer, Cardiac digital twins: a tool to investigate the function and treatment of the diabetic heart, Cardiovasc. Diabetol. 24 (2025) 293.
[19] C. Sadée, S. Testa, T. Barba, K. Hartmann, M. Schuessler, A. Thieme, G.M. Church, I. Okoye, T. Hernandez-Boussard, L. Hood, others, Medical digital twins: enabling precision medicine and medical artificial intelligence, Lancet Digit. Health (2025).
[20] M.H. Li, “Digital Twin” Intelligent Management of Large Medical Equipment Based On Internet of Things Platform, J. Med. Imaging Radiat. Sci. 55 (2024) 101481.
[21] T. Sun, J. Lu, Q. Li, Y. Kou, Can digital twin technology revolutionize public health emergency management? Insights from the PPRR framework, Front. Public Health 13 (2025) 1631339.