Digital Twins for Precision Healthcare

Ahmadi-Assalemi, G.; Al-Khateeb, H.; Maple, C.; Epiphaniou, G.; Alhaboby, Z. A.; Alkaabi, S. and Alhaboby, D. (2020). Digital Twins for Precision Healthcare. In: Jahankhani, H.; Kendzierskyj, S.; Chelvachandran, N. and Ibarra, J. eds. Cyber Defence in the Age of AI, Smart Societies and Augmented Humanity. Advanced Sciences and Technologies for Security Applications. Cham.: Springer, pp. 133–158.

DOI: https://doi.org/10.1007/978-3-030-35746-7_8

Abstract

Precision healthcare is an emerging concept that will see technology-driven digital transformation of the health service. It enables customised patient outcomes via the development of novel, targeted medical approaches with a focus on intelligent, data-centric smart healthcare models. Currently, precision healthcare is seen as a challenging model to apply due to the complexity of the healthcare ecosystem, which is a multi-level and multifaceted environment with high real-time interactions among disciplines, practitioners, patients and discrete computer systems. Digital Twins (DT) pairs individual physical artefacts with digital models reflecting their status in real-time. Creating a live-model for healthcare services introduces new opportunities for patient care including better risk assessment and evaluation without disturbing daily activities. In this article, to address design and management in this complexity, we examine recent work in Digital Twins (DT) to investigate the goals of precision healthcare at a patient and healthcare system levels. We further discuss the role of DT to achieve precision healthcare, proposed frameworks, the value of active participation and continuous monitoring, and the cyber-security challenges and ethical implications for this emerging paradigm.

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