Skip to main content Skip to main navigation menu Skip to site footer
Articles
DOI: 10.14254/1795-6889.2025.21-3.10
Submitted: 2025-12-30
Published: 2025-12-30

Digital technologies supporting predictive healthcare: Review of research trends

Curtin University, Australia; Toronto Metropolitan University, Canada; Cardiff Metropolitan University, United Kingdom
Bialystok University of Technology
Curtin University
Bialystok University of Technology
Lublin University of Technology
predictive healthcare digital twin artificial intelligence machine learning big clinical data Internet of Things Internet of Medical Things patient monitoring extended reality

Abstract

This study examines research trends in digital technologies supporting predictive healthcare, with particular attention to the role of digital twins. A structured bibliometric analysis combined with qualitative thematic analysis was conducted using publications indexed in the Scopus and Web of Science databases from 2015 to 2025. The results indicate a clear shift towards integrated, data-driven healthcare solutions, in which digital twins function as central frameworks linking artificial intelligence, machine learning and Internet of Medical Things technologies. Three emerging thematic areas were identified: integrated patient data ecosystems, predictive and preventive digital twins, and digital twin–based treatment planning and patient response simulation. The findings highlight increasing interest in personalised, predictive and simulation-oriented healthcare models. At the same time, the analysis reveals a gap between technological development and routine clinical implementation. The study contributes to a clearer understanding of the evolving structure of this research field and outlines directions for future research and application in predictive healthcare.

Metrics

Metrics Loading ...

References

  1. Addula, S.R., Ramaswamy, Y., Dawadi, D., Khan, Z., Veeramachaneni, P., & Pamidi venkata, A.K. (2025). Blockchain-Enabled Healthcare Optimization: Enhancing Security and Decision-Making Using the Mother Optimization Algorithm. 2025 International Conference on Intelligent and Cloud Computing (ICoICC), 1-8.
  2. Adeniyi, A. E., Jimoh, R. G., Awotunde, J. B., Aworinde, H. O., Falola, P. B., & Ninan, D. O. (2024). Blockchain for secured cybersecurity in emerging healthcare systems. Cybersecurity in Emerging Healthcare Systems. https://doi.org/10.1049/PBHE064E_ch11 DOI: https://doi.org/10.1049/PBHE064E_ch11
  3. Akbarialiabad, H., Pasdar, A., & Murrell, D. F. (2024). Digital twins in dermatology, current status, and the road ahead. npj Digital Medicine, 7, 228. https://doi.org/10.1038/s41746-024-01220-7 DOI: https://doi.org/10.1038/s41746-024-01220-7
  4. Alhamam, N., Hafizur Rahman, M. M., & Aljughaiman, A. (2025). A comprehensive review on cybersecurity of digital twins issues, challenges, and future research directions. IEEE Access, 13, 45106–45124. https://doi.org/10.1109/ACCESS.2025.3545004 DOI: https://doi.org/10.1109/ACCESS.2025.3545004
  5. Andronie, M., Lăzăroiu, G., Iatagan, M., Hurloiu, I., Ștefănescu, R., Dijmărescu, A., & Dijmărescu, I. (2023a). Big data management algorithms, deep learning-based object detection technologies, and geospatial simulation and sensor fusion tools in the Internet of Robotic Things. ISPRS International Journal of Geo-Information, 12(2), 35. https://doi.org/10.3390/ijgi12020035 DOI: https://doi.org/10.3390/ijgi12020035
  6. Andronie, M., Lăzăroiu, G., Karabolevski, O. L., Ștefănescu, R., Hurloiu, I., Dijmărescu, A., & Dijmărescu, I. (2023b). Remote big data management tools, sensing and computing technologies, and visual perception and environment mapping algorithms in the Internet of Robotic Things. Electronics, 12(1), 22. https://doi.org/10.3390/electronics12010022 DOI: https://doi.org/10.3390/electronics12010022
  7. Araghi, S. N., Liu, Z., Sarkar, A., Louge, T., & Karray, M. H. (2025). Digital twin’s anatomy: A cross-sector framework with healthcare validation. IEEE Access, 13, 21306–21334. https://doi.org/10.1109/ACCESS.2025.3528736 DOI: https://doi.org/10.1109/ACCESS.2025.3528736
  8. Arastouei, N., & Khan, M. A. (2025). 6G technology in intelligent healthcare: Smart health and its security and privacy perspectives. IEEE Wireless Communications, 32(1), 116–121. https://doi.org/10.1109/MWC.001.2400026 DOI: https://doi.org/10.1109/MWC.001.2400026
  9. Ardelean, A., Balta, D.-F., Neamțu, C., Neamțu, A. A., Roșu, M., & Totolici, B. (2024). Personalized and predictive strategies for diabetic foot ulcer prevention and therapeutic management: Potential improvements through introducing artificial intelligence and wearable technology. Medicine and Pharmacy Reports, 97(4), 419–428. https://doi.org/10.15386/mpr-2818 DOI: https://doi.org/10.15386/mpr-2818
  10. Arunprasath, S., & Annamalai, S. (2024). Improving patient centric data retrieval and cyber security in healthcare: privacy preserving solutions for a secure future. Multimedia Tools and Applications, 83, 70289–70319. https://doi.org/10.1007/s11042-024-18253-5 DOI: https://doi.org/10.1007/s11042-024-18253-5
  11. Asciak, L., Kyeremeh, J., Luo, X., Kazakidi, A., Connolly, P., Picard, F., O’Neill, K., Tsaftaris, S. A., Stewart, G. D., & Shu, W. (2025). Digital twin assisted surgery, concept, opportunities, and challenges. npj Digital Medicine, 8, 32. https://doi.org/10.1038/s41746-024-01413-0 DOI: https://doi.org/10.1038/s41746-024-01413-0
  12. Badjatia, N., Podell, J., Felix, R. B., Chen, L. K., Dalton, K., Wang, T. I., Yang, S., & Hu, P. (2025). Machine learning approaches to prognostication in traumatic brain injury. Current Neurology and Neuroscience Reports, 25, 19. https://doi.org/10.1007/s11910-025-01405-x DOI: https://doi.org/10.1007/s11910-025-01405-x
  13. Baron, R., & Haick, H. (2024). Mobile diagnostic clinics. ACS Sensors, 9(6), 2777–2792. https://doi.org/10.1021/acssensors.4c00636 DOI: https://doi.org/10.1021/acssensors.4c00636
  14. Bellavista, P., & Di Modica, G. (2024). IoTwins: Implementing distributed and hybrid digital twins in industrial manufacturing and facility management settings. Future Internet, 16(2), 65. https://doi.org/10.3390/fi16020065 DOI: https://doi.org/10.3390/fi16020065
  15. Benzidia, S., Makaoui, N., & Bentahar, O. (2021). The impact of big data analytics and artificial intelligence on green supply chain process integration and hospital environmental performance. Technological Forecasting and Social Change, 165, 120557. https://doi.org/10.1016/j.techfore.2020.120557 DOI: https://doi.org/10.1016/j.techfore.2020.120557
  16. Bhagirath, P., Strocchi, M., Bishop, M. J., Boyle, P. M., & Plank, G. (2024). From bits to bedside: entering the age of digital twins in cardiac electrophysiology. EP Europace, 26(12), euae295. https://doi.org/10.1093/europace/euae295 DOI: https://doi.org/10.1093/europace/euae295
  17. Bongomin, O., Mwape, M. C., Mpofu, N. S., Bahunde, B. K., Kidega, R., Mpungu, I. L., Tumusiime, G., Owino, C. A., Goussongtogue, Y. M., Yemane, A., Kyokunzire, P., Malanda, C., Komakech, J., Tigalana, D., Gumisiriza, O., & Ngulube, G. (2025). Digital twin technology advancing industry 4.0 and industry 5.0 across sectors. Results in Engineering, 26, 105583. https://doi.org/10.1016/j.rineng.2025.105583 DOI: https://doi.org/10.1016/j.rineng.2025.105583
  18. Böttcher, L., Fonseca, L. L., & Laubenbacher, R. C. (2025). Control of medical digital twins with artificial neural networks. Philosophical Transactions of the Royal Society A, 383, 20240228. https://doi.org/10.1098/rsta.2024.0228 DOI: https://doi.org/10.1098/rsta.2024.0228
  19. Boussi Rahmouni, H., Hassine, N. B. E. H., Chouchen, M., Ceylan, H. İ., Muntean, R. I., Bragazzi, N. L., & Dergaa, I. (2025). Healthcare 5.0-driven clinical intelligence: The Learn–Predict–Monitor–Detect–Correct framework for systematic artificial intelligence integration in critical care. Healthcare, 13(20), 2553. https://doi.org/10.3390/healthcare13202553 DOI: https://doi.org/10.3390/healthcare13202553
  20. Cappon, G., & Facchinetti, A. (2024). Digital twins in Type 1 Diabetes: A systematic review. Journal of Diabetes Science and Technology. https://doi.org/10.1177/19322968241262112 DOI: https://doi.org/10.1177/19322968241262112
  21. Chakshu, N. K., & Nithiarasu, P. (2024). Orbital learning: a novel, actively orchestrated decentralised learning for healthcare. Scientific Reports, 14, 10459. https://doi.org/10.1038/s41598-024-60915-9 DOI: https://doi.org/10.1038/s41598-024-60915-9
  22. Chen, H., He, D., Xiong, K., Zhao, X., Fang, Z., Zou, R., Zhi, J., & Zhang, Z. (2025a). An AI-enabled self-sustaining sensing lower-limb motion detection system for HMI in the metaverse. Nano Energy, 136, 110724. https://doi.org/10.1016/j.nanoen.2025.110724 DOI: https://doi.org/10.1016/j.nanoen.2025.110724
  23. Chen, Z., Hao, J., Sun, H., Li, M., Zhang, Y., & Qian, Q. (2025b). Applications of digital health technologies and artificial intelligence algorithms in COPD: A systematic review. BMC Medical Informatics and Decision Making, 25(1), 77. https://doi.org/10.1186/s12911-025-02870-7 DOI: https://doi.org/10.1186/s12911-025-02870-7
  24. de Oliveira El-Warrak, L., & Miceli de Farias, C. (2025). Could digital twins be the next revolution in healthcare? European Journal of Public Health, 35(1), 19–25. https://doi.org/10.1093/eurpub/ckae191 DOI: https://doi.org/10.1093/eurpub/ckae191
  25. Delerm, F., & Pilottin, A. (2024). Double edged tech: navigating the public health and legal challenges of digital twin technology. European Journal of Public Health, 34(S3), ckae144.1510. https://doi.org/10.1093/eurpub/ckae144.1510 DOI: https://doi.org/10.1093/eurpub/ckae144.1510
  26. Fatouros, P., Tsirmpas, C., Andrikopoulos, D., Kaplow, S., Kontoangelos, K., & Papageorgiou, C. (2025). Randomized controlled study of a digital data driven intervention for depressive and generalized anxiety symptoms. npj Digital Medicine, 8, 113. https://doi.org/10.1038/s41746-025-01511-7 DOI: https://doi.org/10.1038/s41746-025-01511-7
  27. Fitzpatrick, P. J. (2023). Improving health literacy using the power of digital communications to achieve better health outcomes for patients and practitioners. Frontiers in Digital Health, 5, 1264780. https://doi.org/10.3389/fdgth.2023.1264780 DOI: https://doi.org/10.3389/fdgth.2023.1264780
  28. Gana, D., & Jamil, F. (2025). DAG-based swarm learning approach in healthcare: A survey. IEEE Access, 13, 13796–13815. https://doi.org/10.1109/ACCESS.2025.3531216 DOI: https://doi.org/10.1109/ACCESS.2025.3531216
  29. Ghaempanah, F., Moasses Ghafari, B., Hesami, D., Zadeh, R. H., Noroozpoor, R., Ghalibaf, A. M., & Hasanabadi, P. (2024). Metaverse and its impact on medical education and health care system: a narrative review. Health Science Reports, 7(9), e70100. https://doi.org/10.1002/hsr2.70100 DOI: https://doi.org/10.1002/hsr2.70100
  30. Giuffrè, M., & Shung, D. L. (2023). Harnessing the power of synthetic data in healthcare: Innovation, application, and privacy. npj Digital Medicine, 6(1), 186. https://doi.org/10.1038/s41746-023-00927-3 DOI: https://doi.org/10.1038/s41746-023-00927-3
  31. Halder, S., Lawrence, M. C., Testa, G., & Periwal, V. (2025). Donor-specific digital twin for living donor liver transplant recovery. Biology Methods and Protocols, 10(1), bpaf037. https://doi.org/10.1093/biomethods/bpaf037 DOI: https://doi.org/10.1093/biomethods/bpaf037
  32. Hu, H., & Zheng, X. (2024). Augmented and virtual reality-based cyber twin model for observing infants in intensive care: 6G for smart Healthcare 4.0 by machine learning techniques. Wireless Personal Communications. https://doi.org/10.1007/s11277-024-11043-0 DOI: https://doi.org/10.1007/s11277-024-11043-0
  33. Jain, A., Garg, M., Gupta, A., Batra, S., & Narwal, B. (2024). IoMT-BADT: A blockchain-envisioned secure architecture with a lightweight authentication scheme for the Digital Twin environment in the Internet of Medical Things. The Journal of Supercomputing, 80, 16222–16253. https://doi.org/10.1007/s11227-024-06026-8 DOI: https://doi.org/10.1007/s11227-024-06026-8
  34. Jameil, A. K., & Al-Raweshidy, H. (2024a). Enhancing offloading with cybersecurity in edge computing for digital twin-driven patient monitoring. IET Wireless Sensor Systems, 14(6), 363–380. https://doi.org/10.1049/wss2.12086 DOI: https://doi.org/10.1049/wss2.12086
  35. Jameil, A. K., & Al-Raweshidy, H. (2024b). Implementation and evaluation of digital twin framework for Internet of Things based healthcare systems. IET Wireless Sensor Systems, 14(6), 507–527. https://doi.org/10.1049/wss2.12101 DOI: https://doi.org/10.1049/wss2.12101
  36. Jameil, A. K., & Al-Raweshidy, H. (2025). A digital twin framework for real-time healthcare monitoring: Leveraging AI and secure systems for enhanced patient outcomes. Discover Internet of Things, 5(1), 37. https://doi.org/10.1007/s43926-025-00135-3 DOI: https://doi.org/10.1007/s43926-025-00135-3
  37. Jean-Quartier, C., Stryeck, S., Thien, A., Vrella, B., Kleinschuster, J., Spreitzer, E., Wali, M., Mueller, H., Holzinger, A., & Jeanquartier, F. (2024). Unlocking biomedical data sharing: A structured approach with digital twins and artificial intelligence (AI) for open health sciences. Digital Health, 10, 20552076241271769. https://doi.org/10.1177/20552076241271769 DOI: https://doi.org/10.1177/20552076241271769
  38. Jeong, E., & Lee, D. (2025). Metaverse applications in healthcare: opportunities and challenges. Service Business, 19, 4. https://doi.org/10.1007/s11628-024-00577-9 DOI: https://doi.org/10.1007/s11628-024-00577-9
  39. Khan, S., Anwar, U., Khan, A., & Arslan, T. (2025). RF-based sensing and AI decision support for stroke patient monitoring: A digital twin approach. IEEE Access, 13, 74047–74061. https://doi.org/10.1109/ACCESS.2025.3564887 DOI: https://doi.org/10.1109/ACCESS.2025.3564887
  40. Kimpton, L. M., Paun, L. M., Colebank, M. J., & Volodina, V. (2025). Challenges and opportunities in uncertainty quantification for healthcare and biological systems. Philosophical Transactions of the Royal Society A, 383, 20240232. https://doi.org/10.1098/rsta.2024.0232 DOI: https://doi.org/10.1098/rsta.2024.0232
  41. Korzeb, Z., Niedziółka, P., Szpilko, D., & di Pietro, F. (2024). ESG and climate-related risks versus traditional risks in commercial banking: A bibliometric and thematic review. Future Business Journal, 10, 1–22. https://doi.org/10.1186/s43093-024-00392-8 DOI: https://doi.org/10.1186/s43093-024-00392-8
  42. Kulkarni, C., Quraishi, A., Raparthi, M., Shabaz, M., Khan, M. A., Varma, R. A., Keshta, I., Soni, M., & Byeon, H. (2024). Hybrid disease prediction approach leveraging digital twin and metaverse technologies for health consumer. BMC Medical Informatics and Decision Making, 24, 92. https://doi.org/10.1186/s12911-024-02495-2 DOI: https://doi.org/10.1186/s12911-024-02495-2
  43. Kumar Jagatheesaperumal, S., Sathikumar, P., & Rajan, H. (2024). MetaDigiHuman: Haptic interfaces for digital humans in the metaverse. IT Professional, 26(6), 21–27. https://doi.org/10.1109/MITP.2024.3466525 DOI: https://doi.org/10.1109/MITP.2024.3466525
  44. Kumar, A., Dewan, R., Subhi Al-Dayyeni, W., Bhushan, B., Giri, J., Islam, S. M. N., & Elaraby, A. (2025). Wireless body area network: Architecture and security mechanism for healthcare using internet of things. International Journal of Engineering Business Management, 17, 18479790251315317. https://doi.org/10.1177/18479790251315317 DOI: https://doi.org/10.1177/18479790251315317
  45. Lakhan, A., Mohammed, M. A., Zebar, D. A., Abdulkareem, K. H., Deveci, M., & Marhoon, H. A. (2024). DT-LSMAS: Digital twin-assisted large-scale multiagent system for healthcare workflows. IEEE Systems Journal, 18(4), 1883–1892. https://doi.org/10.1109/JSYST.2024.3424259 DOI: https://doi.org/10.1109/JSYST.2024.3424259
  46. Lăzăroiu, G., Androniceanu, A., Grecu, I., Grecu, G., & Neguriță, O. (2022a). Artificial intelligence-based decision-making algorithms, Internet of Things sensing networks, and sustainable cyber-physical management systems in big data-driven cognitive manufacturing. Oeconomia Copernicana, 13(4), 1047–1080. https://doi.org/10.24136/oc.2022.030 DOI: https://doi.org/10.24136/oc.2022.030
  47. Lăzăroiu, G., Andronie, M., Iatagan, M., Geamănu, M., Ștefănescu, R., & Dijmărescu, I. (2022b). Deep learning-assisted smart process planning, robotic wireless sensor networks, and geospatial big data management algorithms in the Internet of Manufacturing Things. ISPRS International Journal of Geo-Information, 11(5), 277. https://doi.org/10.3390/ijgi11050277 DOI: https://doi.org/10.3390/ijgi11050277
  48. Li, D., Chen, X., Li, Q., Zhu, F., Lu, X., & Routray, S. (2024a). Intelligent biomedical photoplethysmography signal cycle division with digital twin in metaverse for consumer health. IEEE Transactions on Consumer Electronics, 70(1), 2116–2128. https://doi.org/10.1109/TCE.2024.3375920 DOI: https://doi.org/10.1109/TCE.2024.3375920
  49. Li, X., Loscalzo, J., Mahmud, A.K.M.F., Aly, D. M., Rzhetsky, A., Zitnik, M., & Benson, M. (2025). Digital twins as global learning health and disease models for preventive and personalized medicine. Genome Medicine, 17, 11. https://doi.org/10.1186/s13073-025-01435-7 DOI: https://doi.org/10.1186/s13073-025-01435-7
  50. Li, Y., Gunasekeran, D. V., RaviChandran, N., Tan, T. F., Ong, J. C. L., Thirunavukarasu, A. J., Polascik, B. W., Habash, R., Khaderi, K., & Ting, D. S. W. (2024b). The next generation of healthcare ecosystem in the metaverse. Biomedical Journal, 47(3), 100679. https://doi.org/10.1016/j.bj.2023.100679 DOI: https://doi.org/10.1016/j.bj.2023.100679
  51. Ling, A., & Butakov, S. (2024). Trust framework for self-sovereign identity in metaverse healthcare applications. Data Science and Management, 7(4), 304–313. https://doi.org/10.1016/j.dsm.2024.04.003 DOI: https://doi.org/10.1016/j.dsm.2024.04.003
  52. Liu, C., Gu, R., Yang, J., Luo, L., Chen, M., Xiong, Y., Huo, Z., Liu, Y., Zhang, K., Gong, J., Wei, L., Lei, Y., Wang, Z. L., & Sun, Q. (2024). A self-powered dual ratchet angle sensing system for digital twins and smart healthcare. Advanced Functional Materials, 34(42), 2405104. https://doi.org/10.1002/adfm.202405104 DOI: https://doi.org/10.1002/adfm.202405104
  53. Ma, Y., Li, Y., Liu, X., Gao, J., Wang, A., Chen, H., Liu, Z., & Jin, Z. (2024). Future perspectives of digital twin technology in orthodontics. Displays, 85, 102818. https://doi.org/10.1016/j.displa.2024.102818 DOI: https://doi.org/10.1016/j.displa.2024.102818
  54. Mamoshina, P., Ojomoko, L., Yanovich, Y., Ostrovski, A., Botezatu, A., Prikhodko, P., Izumchenko, E., Aliper, A., Romantsov, K., Zhebrak, A., Ogu, I. O., & Zhavoronkov, A. (2018). Converging blockchain and next-generation artificial intelligence technologies to decentralize and accelerate biomedical research and healthcare. Oncotarget, 9(5), 5665–5690. https://doi.org/10.18632/oncotarget.22345 DOI: https://doi.org/10.18632/oncotarget.22345
  55. Marques, L., Costa, B., Pereira, M., Silva, A., Santos, J., Saldanha, L., Silva, I., Magalhães, P., Schmidt, S., & Vale, N. (2024). Advancing precision medicine: A review of innovative in silico approaches for drug development, clinical pharmacology and personalized healthcare. Pharmaceutics, 16(3), 332. https://doi.org/10.3390/pharmaceutics16030332 DOI: https://doi.org/10.3390/pharmaceutics16030332
  56. McCourt, C. (2024). Exploring the intersection of the medical metaverse and healthcare ethics: future considerations and caveats. Global Health Journal, 8(1), 36–40. https://doi.org/10.1016/j.glohj.2024.02.005 DOI: https://doi.org/10.1016/j.glohj.2024.02.005
  57. Mihai, S., Yaqoob, M., Hung, D., Davis, W., Towakel, P., Raza, M., Karamanoglu, M., Barn, B., Shetve, D., Prasad, R., Venkataraman, H., Trestian, R., & Nguyen, H. X. (2022). Digital twins: A survey on enabling technologies, challenges, trends and future prospects. IEEE Communications Surveys and Tutorials, 24(4), 2255–2291. https://doi.org/10.1109/COMST.2022.3208773 DOI: https://doi.org/10.1109/COMST.2022.3208773
  58. Mistry, K., & Dafoulas, G. (2025). IoT wearables in child health: A comprehensive scoping review and exploration of ubiquitous computing. Internet of Things, 31, 101556. https://doi.org/10.1016/j.iot.2025.101556 DOI: https://doi.org/10.1016/j.iot.2025.101556
  59. Morone, G., Ciancarelli, I., Calabrò, R. S., Cerasa, A., Iosa, M., & Gimigliano, F. (2025). MetaRehabVerse: The great opportunity to put the person’s functioning and participation at the center of Healthcare. Neurorehabilitation and Neural Repair, 39(3), 241–255. https://doi.org/10.1177/15459683241309587 DOI: https://doi.org/10.1177/15459683241309587
  60. Mosquera-Lopez, C., & Jacobs, P. G. (2024). Digital twins and artificial intelligence in metabolic disease research. Trends in Endocrinology & Metabolism, 35(6), 549–557. https://doi.org/10.1016/j.tem.2024.04.019 DOI: https://doi.org/10.1016/j.tem.2024.04.019
  61. Nadeem, M., Kostic, S., Dornhöfer, M., Weber, C., & Fathi, M. (2025). A comprehensive review of digital twin in healthcare in the scope of simulative health-monitoring. Digital Health, 11, 1–25. https://doi.org/10.1177/20552076241304078 DOI: https://doi.org/10.1177/20552076241304078
  62. Nair, R. R., Rattan, P., Kumar, M., & Bhardwaj, V. (2025). Predictive BlockVax distribution: Enhancing healthcare supply chain resilience with blockchain and LSTM. International Journal of Computational Intelligence Systems, 18(1), 159. https://doi.org/10.1007/s44196-025-00897-2 DOI: https://doi.org/10.1007/s44196-025-00897-2
  63. Nankya, M., Mugisa, A., Usman, Y., Upadhyay, A., & Chataut, R. (2024). Security and privacy in e-health systems: A review of AI and machine learning techniques. IEEE Access, 12, 148796–148816. https://doi.org/10.1109/ACCESS.2024.3469215 DOI: https://doi.org/10.1109/ACCESS.2024.3469215
  64. Narigina, M., Romanovs, A., & Bruzgiene, R. (2024). Digital Twin Technology in Healthcare: A Literature Review. 2024 IEEE 11th Workshop on Advances in Information, Electronic and Electrical Engineering (AIEEE), Valmiera, Latvia, 1-8. https://doi.org/10.1109/AIEEE62837.2024.10586661. DOI: https://doi.org/10.1109/AIEEE62837.2024.10586661
  65. Núñez-Merino, M., Maqueira-Marín, J. M., Moyano-Fuentes, J., & Castaño-Moraga, C. A. (2024). Product development process for a new healthcare service in the Industry 4.0 context: an action research approach. Production Planning & Control, 36(2), 177–196. https://doi.org/10.1080/09537287.2024.2349224 DOI: https://doi.org/10.1080/09537287.2024.2349224
  66. Otoom, S. (2025). Risk auditing for digital twins in cyber physical systems: A systematic review. Journal of Cyber Security and Risk Analysis, 2025(1), 22–35. https://doi.org/10.63180/jcsra.thestap.2025.1.3 DOI: https://doi.org/10.63180/jcsra.thestap.2025.1.3
  67. Pataca, A. O., Zdravevski, E., Coelho, P. J., Garcia, N. M., Deryuck, M., Albuquerque, C., & Pires, I. M. (2025). Use of machine learning for predicting stress episodes based on wearable sensor data: A systematic review. Computers in Biology and Medicine, 198, 111166. https://doi.org/10.1016/j.compbiomed.2025.111166 DOI: https://doi.org/10.1016/j.compbiomed.2025.111166
  68. Peiffer-Smadja, N., Rawson, T. M., Ahmad, R., Buchard, A., Pantelis, G., Lescure, F.-X., Birgand, G., & Holmes, A. H. (2020). Machine learning for clinical decision support in infectious diseases: A narrative review of current applications. Clinical Microbiology and Infection, 26(5), 584–595. https://doi.org/10.1016/j.cmi.2019.09.009 DOI: https://doi.org/10.1016/j.cmi.2019.09.009
  69. Penverne, Y., Martinez, C., Cellier, N., Pehlivan, C., Jenvrin, J., Savary, D., Debierre, V., Deciron, F., Bichri, A., Lebastard, Q., Montassier, E., Leclere, B., & Fontanili, F. (2024). A simulation based digital twin approach to assessing the organization of response to emergency calls. npj Digital Medicine, 7, 385. https://doi.org/10.1038/s41746-024-01392-2 DOI: https://doi.org/10.1038/s41746-024-01392-2
  70. Pinto, A., Pennisi, F., Odelli, S., de Ponti, E., Veronese, N., Signorelli, C., Baldo, V., & Gianfredi, V. (2025). Artificial intelligence in the management of infectious diseases in older adults: Diagnostic, prognostic, and therapeutic applications. Biomedicines, 13(10), 2525. https://doi.org/10.3390/biomedicines13102525 DOI: https://doi.org/10.3390/biomedicines13102525
  71. Qiu, J., Lam, K., Li, G., Acharya, A., Wong, T. Y., Darzi, A., Yuan, W., & Topol, E. J. (2024). LLM-based agentic systems in medicine and healthcare. Nature Machine Intelligence, 6, 1418–1420. https://doi.org/10.1038/s42256-024-00944-1 DOI: https://doi.org/10.1038/s42256-024-00944-1
  72. Raamkhumar, M. H., & Swamy, T. (2024). Bibliometric Cartography on Personality Traits and Stress: In Quest of Panaceas for Contemporary Workplace Challenges. Journal of Scientometric Research, 13, 298–316. https://doi.org/10.5530/jscires.13.1.25 DOI: https://doi.org/10.5530/jscires.13.1.25
  73. Rahim, M., Lalouani, W., Toubal, E., & Emokpae, L. (2024). A digital twin-based platform for medical cyber-physical systems. IEEE Access, 12, 174591–174607. https://doi.org/10.1109/ACCESS.2024.3502077 DOI: https://doi.org/10.1109/ACCESS.2024.3502077
  74. Rana, R., Higgins, N., Stedman, T., March, S., Gucciardi, D. F., Barua, P. D., & Joshi, R. (2025). Passive AI detection of stress and burnout among frontline workers. Nursing Reports, 15(11), 373. https://doi.org/10.3390/nursrep15110373 DOI: https://doi.org/10.3390/nursrep15110373
  75. Rehman, A., Abbas, S., Khan, M. A., Ghazal, T. M., Adnan, K. M., & Mosavi, A. (2022). A secure healthcare 5.0 system based on blockchain technology entangled with federated learning technique. Computers in Biology and Medicine, 150, 106019. https://doi.org/10.1016/j.compbiomed.2022.106019 DOI: https://doi.org/10.1016/j.compbiomed.2022.106019
  76. Rizzo, M. (2025). AI in neurology: Everything, everywhere, all at once part 3: Surveillance, synthesis, simulation, and systems. Annals of Neurology. https://doi.org/10.1002/ana.27230 DOI: https://doi.org/10.1002/ana.27230
  77. Sadeghi, M., & Mahmoudi, A. (2024). Synergy between blockchain technology and Internet of Medical Things in healthcare: A way to sustainable society. Information Sciences, 660, 120049. https://doi.org/10.1016/j.ins.2023.120049 DOI: https://doi.org/10.1016/j.ins.2023.120049
  78. Samathoti, P., Rajasekhar, R. K., Bukke, S. P. N., Rajasekhar, E. S. K., Jaiswal, A. A., & Eftekhari, Z. (2025). The role of nanomedicine and artificial intelligence in cancer health care: Individual applications and emerging integrations—A narrative review. Discover Oncology, 16(1), 697. https://doi.org/10.1007/s12672-025-02469-4 DOI: https://doi.org/10.1007/s12672-025-02469-4
  79. Samei, E. (2025). The future of in silico trials and digital twins in medicine. PNAS Nexus, 4(5), pgaf123. https://doi.org/10.1093/pnasnexus/pgaf123 DOI: https://doi.org/10.1093/pnasnexus/pgaf123
  80. Saraswat, D., Bhattacharya, P., Verma, A., Prasad, V. K., Tanwar, S., Sharma, G., Bokoro, P. N., & Sharma, R. (2022). Explainable AI for Healthcare 5.0: Opportunities and challenges. IEEE Access, 10, 84486–84517. https://doi.org/10.1109/ACCESS.2022.3197671 DOI: https://doi.org/10.1109/ACCESS.2022.3197671
  81. Sel, K., Hawkins-Daarud, A., Chaudhuri, A., Osman, D., Bahai, A., Paydarfar, D., Willcox, K., Chung, C., & Jafari, R. (2025). Survey and perspective on verification, validation, and uncertainty quantification of digital twins for precision medicine. npj Digital Medicine, 8, 40 (2025). https://doi.org/10.1038/s41746-025-01447-y DOI: https://doi.org/10.1038/s41746-025-01447-y
  82. Shankar, R., Wang, L., Hoe, H. S., Liew, M. F., Gollamudi, S. P. K., & Wong, S. (2025). Role of artificial intelligence in virtual emergency care: A protocol for a systematic review. BMJ Open, 15(9), e103084. https://doi.org/10.1136/bmjopen-2025-103084 DOI: https://doi.org/10.1136/bmjopen-2025-103084
  83. Sharma, A. K., Srivastava, M. K., & Sharma, R. (2025). Barriers and challenges for digital twin adoption in healthcare supply chain and operations management. Global Business Review. https://doi.org/10.1177/09721509251314795 DOI: https://doi.org/10.1177/09721509251314795
  84. Shehzad, N., Ramtiyal, B., Jabeen, F., Mangla, S. K., & Vijayvargy, L. (2024). Metaverse adoption as a cornerstone for sustainable healthcare firms in the industry 5.0 epoch. Journal of Enterprise Information Management, 37(4), 1254–1281. https://doi.org/10.1108/JEIM-10-2023-0559 DOI: https://doi.org/10.1108/JEIM-10-2023-0559
  85. Shen, S., Qi, W., Liu, X., Zeng, J., Li, S., Zhu, X., Dong, C., Wang, B., Shi, Y., Yao, J., Wang, B., Jing, L., Cao, S., & Liang, G. (2025). From virtual to reality: innovative practices of digital twins in tumor therapy. Journal of Translational Medicine, 23, 348. https://doi.org/10.1186/s12967-025-06371-z DOI: https://doi.org/10.1186/s12967-025-06371-z
  86. Shoukat, M. U., Yan, L., Zhang, J., Cheng, Y., Raza, M. U., & Niaz, A. (2024). Smart home for enhanced healthcare: exploring human machine interface oriented digital twin model. Multimedia Tools and Applications, 83, 31297–31315. https://doi.org/10.1007/s11042-023-16875-9 DOI: https://doi.org/10.1007/s11042-023-16875-9
  87. Smokovski, I., Steinle, N., Behnke, A., Bhaskar, S. M. M., Grech, G., Richter, K., Niklewski, G., Birkenbihl, C., Parini, P., Andrews, R. J., Bauchner, H., & Golubnitschaja, O. (2024). Digital biomarkers: 3PM approach revolutionizing chronic disease management—EPMA 2024 position. EPMA Journal, 15(2), 149–162. https://doi.org/10.1007/s13167-024-00364-6 DOI: https://doi.org/10.1007/s13167-024-00364-6
  88. Ștefănigă, S. A., Cordoș, A. A., Ivascu, T., Feier, C. V. I., Muntean, C., Stupinean, C. V., Călinici, T., Aluaș, M., & Bolboacă, S. D. (2024). Advancing Precision Oncology with Digital and Virtual Twins: A Scoping Review. Cancers, 16(22), 3817. https://doi.org/10.3390/cancers16223817 DOI: https://doi.org/10.3390/cancers16223817
  89. Sultanpure, K. A., Bagade, J., Bangare, S. L., Bangare, M. L., Bamane, K. D., & Patankar, A. J. (2024). Internet of things and deep learning based digital twins for diagnosis of brain tumor by analyzing MRI images. Measurement: Sensors, 33, 101220. https://doi.org/10.1016/j.measen.2024.101220 DOI: https://doi.org/10.1016/j.measen.2024.101220
  90. Tana, C., Siniscalchi, C., Cerundolo, N., Meschi, T., Martelletti, P., Tana, M., Moffa, L., Wells-Gatnik, W., Cipollone, F., & Giamberardino, M. A. (2025). Smart aging: Integrating AI into elderly healthcare. BMC Geriatrics, 25(1), 1024. https://doi.org/10.1186/s12877-025-06723-w DOI: https://doi.org/10.1186/s12877-025-06723-w
  91. Thangaraj, P. M., Benson, S. H., Oikonomou, E. K., Asselbergs, F. W., & Khera, R. (2024). Cardiovascular care with digital twin technology in the era of generative artificial intelligence. European Heart Journal, 45(45), 4808–4821. https://doi.org/10.1093/eurheartj/ehae619 DOI: https://doi.org/10.1093/eurheartj/ehae619
  92. Trayanova, N. A., & Prakosa, A. (2024). Up digital and personal: How heart digital twins can transform heart patient care. Heart Rhythm, 21(1), 89–99. https://doi.org/10.1016/j.hrthm.2023.10.019 DOI: https://doi.org/10.1016/j.hrthm.2023.10.019
  93. Upadrista, V., Nazir, S., & Tianfield, H. (2025). Blockchain-enabled digital twin system for brain stroke prediction. Brain Informatics, 12, 1. https://doi.org/10.1186/s40708-024-00247-6 DOI: https://doi.org/10.1186/s40708-024-00247-6
  94. Vaughan Robinson, A., Noël, J., Peckham-Cooper, A., & Pegna, V. (2025). How can the model for a sustainable surgical pathway be enhanced by digital medicine? Surgery (Oxford), 43(3), 153–159. https://doi.org/10.1016/j.mpsur.2024.12.004 DOI: https://doi.org/10.1016/j.mpsur.2024.12.004
  95. Wang, H., Arulraj, T., Ippolito, A., & Popel, A. S. (2024). From virtual patients to digital twins in immuno-oncology: lessons learned from mechanistic quantitative systems pharmacology modeling. npj Digital Medicine, 7, 189. https://doi.org/10.1038/s41746-024-01188-4 DOI: https://doi.org/10.1038/s41746-024-01188-4
  96. Wasilewski, T., Kamysz, W., & Gȩbicki, J. (2024). AI-assisted detection of biomarkers by sensors and biosensors for early diagnosis and monitoring. Biosensors, 14(7), 356. https://doi.org/10.3390/bios14070356 DOI: https://doi.org/10.3390/bios14070356
  97. Wattanachayakul, P., Kittipibul, V., Salah, H. M., Yaku, H., Núñez, J., De La Espriella, R., Biering-Sørensen, T., & Fudim, M. (2024). Non-invasive heart failure monitoring: Leveraging smart scales and digital biomarkers to improve heart failure outcomes. Heart Failure Reviews, 29(5), 1145–1156. https://doi.org/10.1007/s10741-024-10426-6 DOI: https://doi.org/10.1007/s10741-024-10426-6
  98. Wellmann, N., Marc, M. S., Stoicescu, E. R., Pescaru, C. C., Trușculescu, A. A., Martis, F. G., Ciortea, I., Crișan, A. F., Balica, M. A., Velescu, D. R., & Fira-Mladinescu, O. (2024). Enhancing adult asthma management: A review on the utility of remote home spirometry and mobile applications. Journal of Personalized Medicine, 14(8), 852. https://doi.org/10.3390/jpm14080852 DOI: https://doi.org/10.3390/jpm14080852
  99. Wentzel, A., Attia, S., Zhang, X., Canahuate, G., Fuller, C. D., & Marai, G. E. (2025). DITTO: A visual digital twin for interventions and temporal treatment outcomes in head and neck cancer. IEEE Transactions on Visualization and Computer Graphics, 31(1), 65–75. https://doi.org/10.1109/TVCG.2024.3456160 DOI: https://doi.org/10.1109/TVCG.2024.3456160
  100. Wu, C., Lima, E. A. B. F., Stowers, C. E., Xu, Z., Yam, C., Son, J. B., Ma, J., Rauch, G. M., & Yankeelov, T. E. (2025). MRI-based digital twins to improve treatment response of breast cancer by optimizing neoadjuvant chemotherapy regimens. npj Digital Medicine, 8, 195. https://doi.org/10.1038/s41746-025-01579-1 DOI: https://doi.org/10.1038/s41746-025-01579-1
  101. Wu, Y. W., Zhang, K., & Zhang, Y. (2021). Digital twin networks: A survey. IEEE Internet of Things Journal, 8(18), 13789–13804. https://doi.org/10.1109/JIOT.2021.3079510 DOI: https://doi.org/10.1109/JIOT.2021.3079510
  102. Xie, S., Zhan, M., Li, Y., & Xi, F. (2025). The virtual-real interaction system design and interaction characteristics research of an ankle rehabilitation robot based on digital twin. Technology and Health Care. https://doi.org/10.1177/09287329251337237 DOI: https://doi.org/10.1177/09287329251337237
  103. Xing, J., Wang, D., Zhang, L., & Li, L. (2024). Cyber-physical system converged digital twin for secure patient monitoring and attack detection. Wireless Personal Communications. https://doi.org/10.1007/s11277-024-11201-4 DOI: https://doi.org/10.1007/s11277-024-11201-4
  104. Xue, J., Li, Z., & Zhang, S. (2025). Multi-resource constrained elective surgical scheduling with Nash equilibrium toward smart hospitals. Scientific Reports, 15, 3946. https://doi.org/10.1038/s41598-025-87867-y DOI: https://doi.org/10.1038/s41598-025-87867-y
  105. Yigit, Y., Duran, K., Sheykhkanloo, N., Maglaras, L. A., Huynh, N., & Canberk, B. (2024). Machine learning for smart healthcare management using IoT. Studies in Computational Intelligence, 1169, 135–166. https://doi.org/10.1007/978-981-97-5624-7_4 DOI: https://doi.org/10.1007/978-981-97-5624-7_4
  106. Ying, L. P., Yin, O. X., Quan, O. W., Jain, N., Mayuren, J., Pandey, M., Gorain, B., & Candasamy, M. (2025). Continuous glucose monitoring data for artificial intelligence-based predictive glycemic event: A potential aspect for diabetic care. International Journal of Diabetes in Developing Countries 45(2), 272–287. https://doi.org/10.1007/s13410-024-01349-x DOI: https://doi.org/10.1007/s13410-024-01349-x
  107. Yurkovich, J. T., Evans, S. J., Rappaport, N., Boore, J. L., Lovejoy, J. C., Price, N. D., & Hood, L. E. (2024). The transition from genomics to phenomics in personalized population health. Nature Reviews Genetics, 25, 286–302. https://doi.org/10.1038/s41576-023-00674-x DOI: https://doi.org/10.1038/s41576-023-00674-x
  108. Zerrouk, N., Augé, F. & Niarakis, A. (2024). Building a modular and multi-cellular virtual twin of the synovial joint in rheumatoid arthritis. npj Digital Medicine, 7, 379. https://doi.org/10.1038/s41746-024-01396-y DOI: https://doi.org/10.1038/s41746-024-01396-y
  109. Zheng, R., Ng, S. T., Shao, Y., Li, Z., & Xing, J. (2025). Leveraging digital twin for healthcare emergency management system: Recent advances, critical challenges, and future directions. Reliability Engineering & System Safety, 261, 111079. https://doi.org/10.1016/j.ress.2025.111079 DOI: https://doi.org/10.1016/j.ress.2025.111079

How to Cite

Lăzăroiu, G., Szpilko, D., Gedeon, T., Halicka, K., & Rzepka, A. (2025). Digital technologies supporting predictive healthcare: Review of research trends. Human Technology, 21(3), 694–730. https://doi.org/10.14254/1795-6889.2025.21-3.10