Digital Human Twins in the Age of Hyper-Personalisation and Ethical Complexity
Keywords:
Digital Human Twins, Hyper-personalisation, Data EthicAbstract
The rapid rise of Digital Human Twins (DHTs) , essentially virtual replicas of real people that are constantly updated with real-time data, has opened up exciting possibilities while also raising significant ethical questions. Initially rooted in healthcare and precision medicine, the concept of digital twins has now branched out into areas like fitness, fashion, education, and emotional AI, all thanks to advancements in IoT, machine learning, and biometric technologies. This study dives into the intricate relationship between hyper-personalization and ethical considerations, looking at how digital human twins are transforming our sense of identity, autonomy, and social interactions. This research takes a narrative literature review approach, pulling together secondary data from peer-reviewed journals, industry reports, and ethical frameworks. The goal is to critically examine the interdisciplinary challenges that pop up at the crossroads of artificial intelligence, data science, psychology, healthcare, and law. This paper tackles important ethical issues like data privacy, surveillance, emotional manipulation, and informed consent. At the same time, it dives into the complex technology needed to maintain these real-time digital representations. Even though digital human twins are becoming more integrated into our daily lives, they are still not fully understood, particularly regarding their long-term effects on society. The existing academic literature lacks a unified, transdisciplinary framework that critically explores the intersection of emotional AI, biometric data, and digital identity. This paper aims to contribute to the academic conversation by addressing this gap, providing a comprehensive ethical-technological analysis, and emphasizing the urgent need for governance frameworks that prioritize human dignity and rights. As digital companions continue to evolve, this research lays a crucial groundwork for future studies and policymaking that seeks to balance innovation with ethical responsibility.
References
Grieves, M. (2016). Origins of the digital twin concept [Working paper]. Digital Twin Institute. https://doi.org/10.13140/RG.2.2.26367.61609
Gaffinet, B., Al Haj Ali, J., Naudet, Y., & Panetto, H. (2025). Human digital twins: A systematic literature review and concept disambiguation for Industry 5.0. Computers in Industry, 166, 104230. https://doi.org/10.1016/j.compind.2024.104230
Papachristou, K., Katsakiori, P. F., Papadimitroulas, P., Strigari, L., & Kagadis, G. C. (2024). Digital twins' advancements and applications in healthcare, towards precision medicine. Journal of Personalized Medicine, 14(11), 1101. https://doi.org/10.3390/jpm14111101
Aluwala, A. (2020). Integrating AI for real-time user behaviour analysis and reporting. Journal of Scientific and Engineering Research, 7(6), 285–292. https://jsaer.com/download/vol-7-iss-6-2020/JSAER2020-7-6-285-292.pdf
Matcha, A. (2023). Innovations in healthcare: Transforming patient care through technology, personalized medicine, and global health crises. International Journal of Science and Research (IJSR), 12(12). https://dx.doi.org/10.21275/SR231222082955
Kreuzer, T., Papapetrou, P., & Zdravkovic, J. (2024). Artificial intelligence in digital twins—A systematic literature review. Data & Knowledge Engineering, 151, 102304. https://doi.org/10.1016/j.datak.2024.102304
Quach, S., Thaichon, P., Martin, K. D., Weaven, S., & Palmatier, R. W. (2022). Digital technologies: Tensions in privacy and data. Journal of the Academy of Marketing Science, 50(6), 1299–1323. https://doi.org/10.1007/s11747-022-00845-y
Kreuzer, T., Papapetrou, P., & Zdravkovic, J. (2024). Artificial intelligence in digital twins—A systematic literature review. Data & Knowledge Engineering, 151, 102304. https://doi.org/10.1016/j.datak.2024.102304
Durango, I., Penichet, V. M. R., & Gallud, J. A. (2024). Exploring human-data interaction: An AI-enhanced systematic mapping. Universal Access in the Information Society. https://doi.org/10.1007/s10209-024-01179-y
Lim, W. M. (2024). What is qualitative research? An overview and guidelines. Australasian Marketing Journal, 0(0). https://doi.org/10.1177/14413582241264619
Spector, J. M. (2015). Foundations of educational technology: Integrative approaches and interdisciplinary perspectives (2nd ed.). Routledge. https://doi.org/10.4324/9781315764269
Papachristou, K., Katsakiori, P. F., Papadimitroulas, P., Strigari, L., & Kagadis, G. C. (2024a). Digital twins' advancements and applications in healthcare, towards precision medicine. Journal of Personalized Medicine, 14(11), 1101. https://doi.org/10.3390/jpm14111101
Migliaccio, C. (2024, October 21). What are the legal implications of biometric data collection. Warren & Migliaccio.
Kouros, T., & Papa, V. (2024). Digital mirrors: AI companions and the self. Societies, 14(10), 200. https://doi.org/10.3390/soc14100200
De Keyser, A., Bart, Y., Gu, X., Liu, S. Q., Robinson, S. G., & Kannan, P. K. (2021). Opportunities and challenges of using biometrics for business: Developing a research agenda. Journal of Business Research, 136, 52–62. https://doi.org/10.1016/j.jbusres.2021.07.028
De Witte, N. A. J., Joris, S., Van Assche, E., & Van Daele, T. (2021). Technological and digital interventions for mental health and wellbeing: An overview of systematic reviews. Frontiers in Digital Health, 3, 754337. https://doi.org/10.3389/fdgth.2021.754337
Dingorkar, S., Kalshetti, S., Shah, Y., & Lahane, P. (2024). Real-time data processing architectures for IoT applications: A comprehensive review. In 2024 First International Conference on Technological Innovations and Advance Computing (TIACOMP) (pp. 507–513). IEEE. https://doi.org/10.1109/TIACOMP64125.2024.00090
Zainal Abiddin, N., Ibrahim, I., & Abdul Aziz, S. A. (2022). Advocating digital literacy: Community-based strategies and approaches. Academic Journal of Interdisciplinary Studies, 11(1). https://doi.org/10.36941/ajis-2022-0018
von Eschenbach, W. J. (2021). Transparency and the black box problem: Why we do not trust AI. Philosophia Technologica, 34, 1607–1622. https://doi.org/10.1007/s13347-021-00477-0
Smith, M., & Miller, S. (2021). Biometric and non-biometric integration: Dual use dilemmas. In Biometric identification, law and ethics (pp. 57–72). SpringerBriefs in Ethics. Springer. https://doi.org/10.1007/978-3-030-90256-8_4
Attaran, M., & Celik, B. G. (2023). Digital twin: Benefits, use cases, challenges, and opportunities. Decision Analytics Journal, 6, 100165. https://doi.org/10.1016/j.dajour.2023.100165
Matta, A., & Lugaresi, G. (2023). Digital twins: Features, models, and services. In 2023 Winter Simulation Conference (WSC) (pp. 46–60). IEEE. https://doi.org/10.1109/WSC60868.2023.10407260
Zovko, K., Šerić, L., Perković, T., Belani, H., & Šolić, P. (2023). IoT and health monitoring wearable devices as enabling technologies for sustainable enhancement of life quality in smart environments. Journal of Cleaner Production, 413, 137506. https://doi.org/10.1016/j.jclepro.2023.137506
Apte, P. P., & Spanos, C. J. (2024, August 27). How human-informed AI leads to more accurate digital twins. MIT Sloan Management Review. https://sloanreview.mit.edu/article/how-human-informed-ai-leads-to-more-accurate-digital-twins/
Ascone, C., & Vanderhaegen, F. (2022). Towards a holistic framework for digital twins of human-machine systems. IFAC-PapersOnLine, 55(29), 67–72. https://doi.org/10.1016/j.ifacol.2022.10.233
López García, Á., Gómez Mármol, F., Garrido, P., Martínez Pérez, G., & Kiah, M. L. M. (2020). A cloud-based framework for machine learning workloads and applications. IEEE Access, 8, 18681–18692. https://doi.org/10.1109/ACCESS.2020.2964386
Vallée, A. (2023). Digital twin for healthcare systems. Frontiers in Digital Health, 5, 1253050. https://doi.org/10.3389/fdgth.2023.1253050
Rasheed, A., San, O., & Kvamsdal, T. (2020). Digital twin: Values, challenges and enablers from a modeling perspective. IEEE Access, 8, 21980–22012. https://doi.org/10.1109/ACCESS.2020.2970143
Zhang, K., Zhou, H.-Y., Baptista-Hon, D. T., Gao, Y., Liu, X., Oermann, E., Xu, S., Jin, S., Zhang, J., Sun, Z., Yin, Y., Razmi, R. M., Loupy, A., Beck, S., Qu, J., Wu, J., & International Consortium of Digital Twins in Medicine. (2024). Concepts and applications of digital twins in healthcare and medicine. Patterns, 5(8), 101028. https://doi.org/10.1016/j.patter.2024.101028
Papachristou, K., Katsakiori, P. F., Papadimitroulas, P., Strigari, L., & Kagadis, G. C. (2024b). Digital twins' advancements and applications in healthcare, towards precision medicine. Journal of Personalized Medicine, 14(11), 1101. https://doi.org/10.3390/jpm14111101
Li, X., Loscalzo, J., Mahmud, A. K. M. F., & et al. (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
Laso, S., Herrera, J. L., Galán-Jiménez, J., & Berrocal, J. (2025). Human digital twins: Enhancing interactions with digital ecosystems. IEEE Internet Computing, 29(1), 56–64. https://doi.org/10.1109/MIC.2024.3509672
Roman, J. (2024, November 14). The rise of digital twins and why they matter. Medium. https://medium.com/@youness.azzamok/the-rise-of-digital-twins-and-why-they-matter-3b0722b406fc
Manning, G. (2013). Descartes’ healthy machines and the human exception. In D. Garber (Ed.), The mechanization of natural philosophy (Vol. 282, pp. [specific pages if known]). Springer. https://doi.org/10.1007/978-94-007-4345-8_10
Wiener, N. (2019). Cybernetics: Or control and communication in the animal and the machine. The MIT Press. https://doi.org/10.7551/mitpress/11810.001.0001
Piccinini, G. (2004). Functionalism, computationalism, and mental states. Studies in History and Philosophy of Science Part A, 35(4), 811–833. https://doi.org/10.1016/j.shpsa.2004.02.003
Mishra, A. (2024). A comprehensive review of artificial intelligence and machine learning: Concepts, trends, and applications. International Scientific Research Journal, 11(5). https://doi.org/10.32628/IJSRST2411587
Ahmed, E., Yaqoob, I., Hashem, I. A. T., Khan, I., Ahmed, A. I. A., Imran, M., & Vasilakos, A. V. (2017). The role of big data analytics in Internet of Things. Computer Networks, 129(Part 2), 459-471. https://doi.org/10.1016/j.comnet.2017.06.013
Zhang, K., Zhou, H.-Y., Baptista-Hon, D. T., Gao, Y., Liu, X., Oermann, E., Xu, S., Jin, S., Zhang, J., Sun, Z., Yin, Y., Razmi, R. M., Loupy, A., Beck, S., Qu, J., Wu, J., & International Consortium of Digital Twins in Medicine. (2024a). Concepts and applications of digital twins in healthcare and medicine. Patterns, 5(8), 101028. https://doi.org/10.1016/j.patter.2024.101028
Kamel Boulos, M. N., & Zhang, P. (2021). Digital twins: From personalised medicine to precision public health. Journal of Personalized Medicine, 11(8), 745. https://doi.org/10.3390/jpm11080745
Grieves, M. W. (2023). Digital twins: Past, present, and future. In N. Crespi, A. T. Drobot, & R. Minerva (Eds.), The digital twin (pp. 59–72). Springer, Cham. https://doi.org/10.1007/978-3-031-21343-4_4
Ruiu, P., Nitti, M., Pilloni, V., Cadoni, M., Grosso, E., & Fadda, M. (2024). Metaverse & Human Digital Twin: Digital Identity, Biometrics, and Privacy in the Future Virtual Worlds. Multimodal Technologies and Interaction, 8(6), 48. https://doi.org/10.3390/mti8060048
Barresi, G., Pacchierotti, C., Laffranchi, M., & De Michieli, L. (2022). Beyond Digital Twins: Phygital Twins for Neuroergonomics in Human-Robot Interaction. Frontiers in neurorobotics, 16, 913605. https://doi.org/10.3389/fnbot.2022.913605
Papachristou, K., Katsakiori, P. F., Papadimitroulas, P., Strigari, L., & Kagadis, G. C. (2024c). Digital Twins' Advancements and Applications in Healthcare, Towards Precision Medicine. Journal of personalized medicine, 14(11), 1101. https://doi.org/10.3390/jpm14111101
Ruiu, P., Nitti, M., Pilloni, V., Cadoni, M., Grosso, E., & Fadda, M. (2024a). Metaverse & Human Digital Twin: Digital Identity, Biometrics, and Privacy in the Future Virtual Worlds. Multimodal Technologies and Interaction, 8(6), 48. https://doi.org/10.3390/mti8060048
Domínguez-Bolaño, T., Campos, O., Barral, V., Escudero, C. J., & García-Naya, J. A. (2022). An overview of IoT architectures, technologies, and existing open-source projects. Internet of Things, 20, 100626. https://doi.org/10.1016/j.iot.2022.100626
Wu, H., Ji, P., Ma, H., & Xing, L. (2023). A Comprehensive Review of Digital Twin from the Perspective of Total Process: Data, Models, Networks and Applications. Sensors (Basel, Switzerland), 23(19), 8306. https://doi.org/10.3390/s23198306
Es-haghi, M. S., Anitescu, C., & Rabczuk, T. (2024). Methods for enabling real-time analysis in digital twins: A literature review. Computers & Structures, 297, 107342. https://doi.org/10.1016/j.compstruc.2024.107342
Kamel Boulos, M. N., & Zhang, P. (2021). Digital Twins: From Personalised Medicine to Precision Public Health. Journal of Personalized Medicine, 11(8), 745. https://doi.org/10.3390/jpm11080745
Martin, K. D., & Zimmermann, J. (2024). Artificial intelligence and its implications for data privacy. Current Opinion in Psychology, 58, 101829. https://doi.org/10.1016/j.copsyc.2024.101829
Hong, Z. (2024, April 28). Personalized recommendations: How Netflix and Amazon use deep learning to enhance user experience. Medium. https://medium.com/@zhonghong9998/personalized-recommendations-how-netflix-and-amazon-use-deep-learning-to-enhance-user-experience-e7bd6fcd18ff
Ho, D., Quake, S. R., McCabe, E. R. B., Chng, W. J., Chow, E. K., Ding, X., Gelb, B. D., Ginsburg, G. S., Hassenstab, J., Ho, C. M., Mobley, W. C., Nolan, G. P., Rosen, S. T., Tan, P., Yen, Y., & Zarrinpar, A. (2020). Enabling Technologies for Personalized and Precision Medicine. Trends in biotechnology, 38(5), 497–518. https://doi.org/10.1016/j.tibtech.2019.12.021
Gaffinet, B., Al Haj Ali, J., Naudet, Y., & Panetto, H. (2025). Human Digital Twins: A systematic literature review and concept disambiguation for Industry 5.0. Computers in Industry, 166, 104230. https://doi.org/10.1016/j.compind.2024.104230
Clement, T. (2025, February 5). The UX of emotion recognition: Can AI truly read feelings? Medium. https://uxdesign.cc/the-ux-of-emotion-recognition-can-ai-truly-read-feelings-e26f16268e96
Ringeval, M., Etindele Sosso, F. A., Cousineau, M., & Paré, G. (2025). Advancing health care with digital twins: Meta-review of applications and implementation challenges. Journal of Medical Internet Research, 27, e69544. https://doi.org/10.2196/69544
Lembcke, T.-B., Engelbrecht, N., Brendel, A. B., & Kolbe, L. M. (2019). To nudge or not to nudge: Ethical considerations of digital nudging based on its behavioral economics roots. In Proceedings of the European Conference on Information Systems (ECIS).
Subías-Beltrán, P., Pitarch, C., Migliorelli, C., Marte, L., Galofré, M., & Orte, S. (2024). The role of transparency in AI-driven technologies: Targeting healthcare. IntechOpen. https://doi.org/10.5772/intechopen.1007444
Vallée A. (2023). Digital twin for healthcare systems. Frontiers in digital health, 5, 1253050. https://doi.org/10.3389/fdgth.2023.1253050
Elahi, E. (2021, November 15). Data merging: Process, challenges, and best practices for combining data from multiple sources. Data Ladder. https://dataladder.com/merging-data-from-multiple-sources/
Ikezuruora, C. (2024, January 12). Unveiling the nexus: The relationship between transparency and accountability in data privacy. Privacyend. https://www.privacyend.com/relationship-between-transparency-accountability-data-privacy/
Khazanchi, D., & Saxena, M. (2025). Navigating digital human rights in the age of AI: Challenges, theoretical perspectives, and research implications. Journal of Information Technology Case and Application Research, 1(14). https://doi.org/10.1080/15228053.2025.2452028
Hanna, M. G., Pantanowitz, L., Jackson, B., Palmer, O., Visweswaran, S., Pantanowitz, J., Deebajah, M., & Rashidi, H. H. (2025). Ethical and bias considerations in artificial intelligence/machine learning. Modern Pathology, 38(3), 100686. https://doi.org/10.1016/j.modpat.2024.100686
Interaction Design Foundation. (2016, August 16). What is inclusive design? Interaction Design Foundation - IxDF. https://www.interaction-design.org/literature/topics/inclusive-design
Khazanchi, D., & Saxena, M. (2025). Navigating digital human rights in the age of AI: challenges, theoretical perspectives, and research implications. Journal of Information Technology Case and Application Research, 1–14. https://doi.org/10.1080/15228053.2025.2452028
Wiertz, S., & Boldt, J. (2022). Evaluating models of consent in changing health research environments. Medicine, Health Care and Philosophy, 25(2), 269–280. https://doi.org/10.1007/s11019-022-10074-3
Kondrup, E. (2025, April 11). Informed consent, redefined: How AI and big data are changing the rules. The Petrie-Flom Center, Harvard Law School. https://petrieflom.law.harvard.edu/2025/04/11/informed-consent-redefined-how-ai-and-big-data-are-changing-the-rules/
Lipartito, K. (2025). Surveillance Capitalism: Origins, History, Consequences. Histories, 5(1), 2. https://doi.org/10.3390/histories5010002
Adarsh, G. S. (2024, August 29). Can AI ever truly understand human emotions, or will it only simulate them? Medium. https://medium.com/@adarshgs.909/can-ai-ever-truly-understand-human-emotions-or-will-it-only-simulate-them-86b46abac4ae
European Parliament and Council of the European Union. (2016). Regulation (EU) 2016/679 of the European Parliament and of the Council of 27 April 2016 on the protection of natural persons with regard to the processing of personal data and on the free movement of such data, and repealing Directive 95/46/EC (General Data Protection Regulation). https://eur-lex.europa.eu/eli/reg/2016/679/oj/eng
Fontes, C., Carpentras, D., & Mahajan, S. (2024). Human digital twins unlocking Society 5.0? Approaches, emerging risks and disruptions. Ethics and Information Technology, 26, 54. https://doi.org/10.1007/s10676-024-09787-1
Mennella, C., Maniscalco, U., De Pietro, G., & Esposito, M. (2024). Ethical and regulatory challenges of AI technologies in healthcare: A narrative review. Heliyon, 10(4), e26297. https://doi.org/10.1016/j.heliyon.2024.e26297
Gaffinet, B., Al Haj Ali, J., Naudet, Y., & Panetto, H. (2025). Human digital twins: A systematic literature review and concept disambiguation for Industry 5.0. Computers in Industry, 166, 104230. https://doi.org/10.1016/j.compind.2024.104230
Papachristou, K., Katsakiori, P. F., Papadimitroulas, P., Strigari, L., & Kagadis, G. C. (2024d). Digital Twins' Advancements and Applications in Healthcare, Towards Precision Medicine. Journal of personalized medicine, 14(11), 1101. https://doi.org/10.3390/jpm14111101
Gaffinet, B., Al Haj Ali, J., Naudet, Y., & Panetto, H. (2025a). Human digital twins: A systematic literature review and concept disambiguation for Industry 5.0. Computers in Industry, 166, 104230. https://doi.org/10.1016/j.compind.2024.104230
Ascone, C., & Vanderhaegen, F. (2022). Towards a holistic framework for digital twins of human-machine systems. IFAC-PapersOnLine, 55(29), 67–72. https://doi.org/10.1016/j.ifacol.2022.10.233
Fontes, C., Carpentras, D., & Mahajan, S. (2024a). Human digital twins unlocking Society 5.0? Approaches, emerging risks and disruptions. Ethics and Information Technology, 26, Article 54. https://doi.org/10.1007/s10676-024-09787-1
Wilson, S., Tolley, C., Mc Ardle, R., Lawson, L., Beswick, E., Hassan, N., Slight, R., & Slight, S. (2024). Recommendations to advance digital health equity: a systematic review of qualitative studies. NPJ digital medicine, 7(1), 173. https://doi.org/10.1038/s41746-024-01177-7
Fontes, C., Carpentras, D., & Mahajan, S. (2024b). Human digital twins unlocking Society 5.0? Approaches, emerging risks and disruptions. Ethics and Information Technology, 26, Article 54. https://doi.org/10.1007/s10676-024-09787-1
Ascone, C., & Vanderhaegen, F. (2022a). Towards a holistic framework for digital twins of human-machine systems. IFAC-PapersOnLine, 55(29), 67–72. https://doi.org/10.1016/j.ifacol.2022.10.233
OECD. (2023). Regulatory sandboxes in artificial intelligence (OECD Digital Economy Papers, No. 356). OECD Publishing. https://doi.org/10.1787/8f80a0e6-en
Gaffinet, B., Al Haj Ali, J., Naudet, Y., & Panetto, H. (2025a). Human digital twins: A systematic literature review and concept disambiguation for industry 5.0. Computers in Industry, 166, 104230. https://doi.org/10.1016/j.compind.2024.104230
Gesualdo, F., Daverio, M., Palazzani, L., Dimitriou, D., Diez-Domingo, J., Fons-Martinez, J., Jackson, S., Vignally, P., Rizzo, C., & Tozzi, A. E. (2021). Digital tools in the informed consent process: a systematic review. BMC medical ethics, 22(1), 18. https://doi.org/10.1186/s12910-021-00585-8
Nannini, L., Marchiori Manerba, M., & Beretta, I. (2024). Mapping the landscape of ethical considerations in explainable AI research. Ethics and Information Technology, 26(44). https://doi.org/10.1007/s10676-024-09773-7
Iqbal, J. D., & Biller-Andorno, N. (2022). The regulatory gap in digital health and alternative pathways to bridge it. Health Policy and Technology, 11(3), 100663. https://doi.org/10.1016/j.hlpt.2022.100663
Parlangeli, O., & Liston, P. M. (2023). User-centered ethical design: An evolutionary perspective. In A. Marcus, E. Rosenzweig, & M. M. Soares (Eds.), Design, user experience, and usability. HCII 2023. Lecture Notes in Computer Science Vol. 14030. Springer. https://doi.org/10.1007/978-3-031-35699-5_21
Bitomsky, L., Pfitzer, E. C., Nißen, M., & Kowatsch, T. (2024). Advancing health equity and the role of digital health technologies: a scoping review protocol. BMJ open, 14(10), e082336. https://doi.org/10.1136/bmjopen-2023-082336
Graili, P., & Farhoudi, B. (2025). The intersection of digital health and artificial intelligence: Clearing the cloud of uncertainty. Digital health, 11, 20552076251315621. https://doi.org/10.1177/20552076251315621
Gaffinet, B., Al Haj Ali, J., Naudet, Y., & Panetto, H. (2025b). Human digital twins: A systematic literature review and concept disambiguation for Industry 5.0. Computers in Industry, 166, 104230. https://doi.org/10.1016/j.compind.2024.104230
Asad, U., Khan, M., Khalid, A., & Lughmani, W. A. (2023). Human-Centric Digital Twins in Industry: A Comprehensive Review of Enabling Technologies and Implementation Strategies. Sensors (Basel, Switzerland), 23(8), 3938. https://doi.org/10.3390/s23083938
Yew, S. Q., Trivedi, D., Adanan, N. I. H., & Chew, B. H. (2024). Facilitators and barriers of digital health technologies implementation in hospital settings in lower-income and middle-income countries since the COVID-19 pandemic: a scoping review protocol. BMJ open, 14(1), e078508. https://doi.org/10.1136/bmjopen-2023-078508
Burton, J. C., Regala, S., Williams, D., Desai, A., He, H., Aalami, O., Mariano, E. R., Stafford, R. S., & Mudumbai, S. C. (2022). A Comparative Utility Score for Digital Health Tools. Journal of medical systems, 46(6), 34. https://doi.org/10.1007/s10916-022-01821-3
Burton, J. C., Regala, S., Williams, D., Desai, A., He, H., Aalami, O., Mariano, E. R., Stafford, R. S., & Mudumbai, S. C. (2022). A Comparative Utility Score for Digital Health Tools. Journal of medical systems, 46(6), 34. https://doi.org/10.1007/s10916-022-01821-3
Sirigu, G., Carminati, B., & Ferrari, E. (2025). Efficient enforcement of privacy preferences for human digital twins in multi-user/provider scenarios. IEEE Transactions on Privacy. https://doi.org/10.1109/TP.2025.3558737
Langås, E. F., Zafar, M. H., & Sanfilippo, F. (2025). Exploring the synergy of human-robot teaming, digital twins, and machine learning in Industry 5.0: A step towards sustainable manufacturing. Journal of Intelligent Manufacturing. https://doi.org/10.1007/s10845-025-02580-x
Sharma, A., Kosasih, E., Zhang, J., Brintrup, A., & Calinescu, A. (2022). Digital twins: State of the art theory and practice, challenges, and open research questions. Journal of Industrial Information Integration, 30, 100383. https://doi.org/10.1016/j.jii.2022.100383
Evans, N., Miklosik, A., & Du, J. T. (2023). University-industry collaboration as a driver of digital transformation: Types, benefits and enablers. Heliyon, 9(10), e21017. https://doi.org/10.1016/j.heliyon.2023.e21017
Zheng, M., Yan, S., & Xu, S. (2025). Digital Economy, Industry–Academia–Research Collaborative Innovation, and the Development of New-Quality Productive Forces. Sustainability, 17(1), 318. https://doi.org/10.3390/su17010318
Yeong, Z. K. (2021, July 10). Smart regulation: Implementing a responsive regulatory regime in Singapore. Paper presented at The Asia-Pacific Europe Law Institutes Alliance (APELIA) Conference 2021 on Artificial Intelligence (AI), Trade, and the Rule of Law. SSRN. https://doi.org/10.2139/ssrn.5093148
Janssen, M., & van der Voort, H. (2016). Adaptive governance: Towards a stable, accountable and responsive government. Government Information Quarterly, 33(1), 1-5. https://doi.org/10.1016/j.giq.2016.02.003
Janssen, M., & van der Voort, H. (2016a). Adaptive governance: Towards a stable, accountable and responsive government. Government Information Quarterly, 33(1), 1-5. https://doi.org/10.1016/j.giq.2016.02.003
Bhat, R., Kowshik, S., Suresh, S., Alamelu, G., Gite, S., & Albattat, A. (2025). Digital companionship or psychological risk? The role of AI characters in shaping youth mental health. Asian Journal of Psychiatry, 104, 104356. https://doi.org/10.1016/j.ajp.2024.104356











