Authors :
Sunish Vengathattil
Volume/Issue :
Volume 10 - 2025, Issue 4 - April
Google Scholar :
https://tinyurl.com/y7mekt57
Scribd :
https://tinyurl.com/3f67htzf
DOI :
https://doi.org/10.38124/ijisrt/25apr1470
Google Scholar
Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.
Note : Google Scholar may take 15 to 20 days to display the article.
Abstract :
The healthcare sector is undergoing a digital transformation thanks to new technologies, with digital twinning
and generative artificial intelligence (AI) leading the innovation. Digital twins, conceptualized originally as engineering or
manufacturing tools, are increasingly finding their way to the healthcare sector, in response to the growing need for
sophisticated virtual patient representations with scope for modeling several complex biological systems. Empowered by
generative AI, digital twins, as they start to replace static models, open their gates into dynamic, predictive, prescriptive
systems, enabling personalized healthcare delivery, disease modeling, surgical planning, and drug discovery. This paper
reviews the combined potential of AI and digital twin technologies in the healthcare domain. It delivers a comprehensive
view on the present possible applications, benefits, and opportunities of technology while putting in perspective the
challenges regarding data privacy, ethical, computational, and design biases. By intertwining results from various studies
and companies, the research thereby expounds into realizing the positive thrust capability of generative AI digital twins in
influencing the transformation of healthcare delivery toward more stringent, predictive, preventive medicine. The paper
identifies future research directions crucial to confronting current challenges and ensuring the responsible deployment of
these technologies in healthcare systems across the globe.
Keywords :
Generative AI, Digital Twins, Healthcare Innovation, Predictive Diagnostics, Personalized Medicine, Patient Simulation, Data Privacy, Ethical AI, Smart Hospital.
References :
- Chen, J., Shi, Y., Yi, C., Du, H., Kang, J., & Niyato, D. (2024). Generative AI-driven human digital twin in IoT-healthcare: A comprehensive survey. IEEE Internet of Things Journal.
- Mikołajewska, E., Mikołajewski, D., Mikołajczyk, T., & Paczkowski, T. (2025). Generative AI in AI-Based Digital Twins for Fault Diagnosis for Predictive Maintenance in Industry 4.0/5.0. Applied Sciences, 15(6), 3166.
- Huang, Y., Zhang, J., Chen, X., Lam, A. H., & Chen, B. M. (2024, June). From Simulation to Prediction: Enhancing Digital Twins with Advanced Generative AI Technologies. In 2024 IEEE 18th International Conference on Control & Automation (ICCA) (pp. 490-495). IEEE.
- Huang, Y., Zhang, J., Chen, X., Lam, A. H., & Chen, B. M. (2024, June). From Simulation to Prediction: Enhancing Digital Twins with Advanced Generative AI Technologies. In 2024 IEEE 18th International Conference on Control & Automation (ICCA) (pp. 490-495). IEEE.
- Mariam, Z., Niazi, S. K., & Magoola, M. (2024). Unlocking the future of drug development: Generative AI, digital twins, and beyond. B ioMedInformatics, 4(2), 1441-1456.
- Korada, L. (2024). Role of Generative AI in the Digital Twin Landscape and How It Accelerates Adoption. J Artif Intell Mach Learn & Data Sci, 2(1), 902-906.
- Bordukova, M., Makarov, N., Rodriguez-Esteban, R., Schmich, F., & Menden, M. P. (2024). Generative artificial intelligence empowers digital twins in drug discovery and clinical trials. Expert opinion on drug discovery, 19(1), 33-42.
- Łukaniszyn, M., Majka, Ł., Grochowicz, B., Mikołajewski, D., & Kawala-Sterniuk, A. (2024). Digital Twins Generated by Artificial Intelligence in Personalized Healthcare. Applied Sciences, 14(20), 9404.
- Zhang, K., Zhou, H. Y., Baptista-Hon, D. T., Gao, Y., Liu, X., Oermann, E., ... & Wu, J. (2024). Concepts and applications of digital twins in healthcare and medicine. Patterns, 5(8).
- Li, T., Long, Q., Chai, H., Zhang, S., Jiang, F., Liu, H., ... & Li, Y. (2025). Generative AI Empowered Network Digital Twins: Architecture, Technologies, and Applications. ACM Computing Surveys, 57(6), 1-43.
- Balasubhramanyam, A., Ramesh, R., Sudheer, R., & Honnavalli, P. B. (2024). Revolutionizing healthcare: A review unveiling the transformative power of digital twins. IEEE Access.
- Gebreab, S., Musamih, A., Salah, K., Jayaraman, R., & Boscovic, D. (2024). Accelerating Digital Twin Development With Generative AI: A Framework for 3D Modeling and Data Integration. IEEE Access.
- 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.
- Akram, J., Aamir, M., Raut, R., Anaissi, A., Jhaveri, R. H., & Akram, A. (2024). Ai-generated content-as-a-service in iomt-based smart homes: Personalizing patient care with human digital twins. IEEE Transactions on Consumer Electronics.
- Elkefi, S. (2024). Role of Digital Twins, Generative AI, and Extended Reality in Cancer Care; CanConTech, a Human Factors Framework for Technology Connectedness. Hospital Supply Chain: Challenges and Opportunities for Improving Healthcare, 571-585.
- Katsoulakis, E., Wang, Q., Wu, H., Shahriyari, L., Fletcher, R., Liu, J., ... & Deng, J. (2024). Digital twins for health: a scoping review. NPJ digital medicine, 7(1), 77.
- Hao, N., Li, Y., Liu, K., Liu, S., Lu, Y., Xu, B., ... & Zhao, Y. (2024). Artificial intelligence-aided digital twin design: A systematic review. Preprints.
- Kuppusamy, P. (2025). Artificial Intelligence-Powered Digital Twin Predictive Analytics Model for Smart Healthcare System: Leveraging Digital Twins' Potential to Improve Healthcare Outcomes. In AI-Powered Digital Twins for Predictive Healthcare: Creating Virtual Replicas of Humans (pp. 271-324). IGI Global Scientific Publishing.
- Xu, H., Omitaomu, F., Sabri, S., Zlatanova, S., Li, X., & Song, Y. (2024). Leveraging generative AI for urban digital twins: a scoping review on the autonomous generation of urban data, scenarios, designs, and 3D city models for smart city advancement. Urban Informatics, 3(1), 29.
- Mateev, M. (2023, July). Design and Implementation of Cognitive Digital Twins with Generative AI and ChatGPT. In 4th Annual International Conference on Computer & Software Engineering (pp. 17-20).
- Abd Elaziz, M., Al‐qaness, M. A., Dahou, A., Al‐Betar, M. A., Mohamed, M. M., El‐Shinawi, M., ... & Ewees, A. A. (2024). Digital twins in healthcare: Applications, technologies, simulations, and future trends. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 14(6), e1559.
- Mazhar, T., khan, S., Shahzad, T., khan, M. A., Saeed, M. M., Awotunde, J. B., & Hamam, H. (2025). Generative AI, IoT, and blockchain in healthcare: application, issues, and solutions. Discover Internet of Things, 5(1), 5.
- Mazhar, T., khan, S., Shahzad, T., khan, M. A., Saeed, M. M., Awotunde, J. B., & Hamam, H. (2025). Generative AI, IoT, and blockchain in healthcare: application, issues, and solutions. Discover Internet of Things, 5(1), 5.
- Zhang, P., & Kamel Boulos, M. N. (2023). Generative AI in medicine and healthcare: promises, opportunities and challenges. Future Internet, 15(9), 286.
- Shu, M., sun, W., zhang, J., duan, X., & ai, M. (2024). Digital-twin-enabled 6G network autonomy and generative intelligence: Architecture, technologies and applications. Digital Twin, 2, 16.
- Wu, J., & Koelzer, V. H. (2024). Towards generative digital twins in biomedical research. Computational and Structural Biotechnology Journal.
- Savaglio, C., Barbuto, V., Mangione, F., & Fortino, G. (2024). Generative digital twins: A novel approach in the iot edge-cloud continuum. IEEE Internet of Things Magazine.
- Barbiero, P., Vinas Torne, R., & Lió, P. (2021). Graph representation forecasting of patient's medical conditions: toward a digital twin. Frontiers in genetics, 12, 652907.
- Wen, J., Kang, J., Niyato, D., Zhang, Y., & Mao, S. (2024). Sustainable Diffusion-based Incentive Mechanism for Generative AI-driven Digital Twins in Industrial Cyber-Physical Systems. IEEE Transactions on Industrial Cyber-Physical Systems.
- Emmert-Streib, F. (2023). What is the role of ai for digital twins?. AI, 4(3), 721-728.
The healthcare sector is undergoing a digital transformation thanks to new technologies, with digital twinning
and generative artificial intelligence (AI) leading the innovation. Digital twins, conceptualized originally as engineering or
manufacturing tools, are increasingly finding their way to the healthcare sector, in response to the growing need for
sophisticated virtual patient representations with scope for modeling several complex biological systems. Empowered by
generative AI, digital twins, as they start to replace static models, open their gates into dynamic, predictive, prescriptive
systems, enabling personalized healthcare delivery, disease modeling, surgical planning, and drug discovery. This paper
reviews the combined potential of AI and digital twin technologies in the healthcare domain. It delivers a comprehensive
view on the present possible applications, benefits, and opportunities of technology while putting in perspective the
challenges regarding data privacy, ethical, computational, and design biases. By intertwining results from various studies
and companies, the research thereby expounds into realizing the positive thrust capability of generative AI digital twins in
influencing the transformation of healthcare delivery toward more stringent, predictive, preventive medicine. The paper
identifies future research directions crucial to confronting current challenges and ensuring the responsible deployment of
these technologies in healthcare systems across the globe.
Keywords :
Generative AI, Digital Twins, Healthcare Innovation, Predictive Diagnostics, Personalized Medicine, Patient Simulation, Data Privacy, Ethical AI, Smart Hospital.