Digital Twins for Healthcare 4.0—Recent Advances, Architecture, and Open Challenges

Alazab, Mamoun; Khan, Latif U.; Koppu, Srinivas; Iyapparaja, M.; Boobalan, Parimala; Baker, Thar; Maddikunta, Praveen Kumar Reddy; Gadekallu, Thippa Reddy; Aljuhani, Ahamed

doi:10.1109/mce.2022.3208986

Cited by 56 publications

(28 citation statements)

References 14 publications

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“…Operational strategies, staffing, and care models can be examined by establishing a digital twin of the whole hospital in the Metaverse to assess the requirements [143]. In scenarios like lack of beds, the transmission of pathogens, the scheduling of doctors, or the availability of operating rooms, these virtual models in the Metaverse can help.…”

Section: F Digital Twinmentioning

confidence: 99%

Metaverse for Healthcare: A Survey on Potential Applications, Challenges and Future Directions

et al. 2023

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The rapid progress in digitalization and automation have led to an accelerated growth in healthcare, generating novel models that are creating new channels for rendering treatment at reduced cost. The Metaverse is an emerging technology in the digital space which has huge potential in healthcare, enabling realistic experiences to the patients as well as the medical practitioners. The Metaverse is a confluence of multiple enabling technologies such as artificial intelligence, virtual reality, augmented reality, internet of medical devices, robotics, quantum computing, etc. through which new directions for providing quality healthcare treatment and services can be explored. The amalgamation of these technologies ensures immersive, intimate and personalized patient care. It also provides adaptive intelligent solutions that eliminates the barriers between healthcare providers and receivers. This article provides a comprehensive review of the Metaverse for healthcare, emphasizing on the state of the art, the enabling technologies to adopt the Metaverse for healthcare, the potential applications, and the related projects. The issues in the adaptation of the Metaverse for healthcare applications are also identified and the plausible solutions are highlighted as part of future research directions.INDEX TERMS Metaverse, healthcare, virtual reality, digital twin, cybersecurity.

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Section: F Digital Twinmentioning

confidence: 99%

Metaverse for Healthcare: A Survey on Potential Applications, Challenges and Future Directions

et al. 2023

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“…The costeffective MEC enables fast remote connection and resource optimization for health monitoring in pervasive healthcare networks [53], teaching/learning activity process in smart classrooms [54], data traffic and congestion management in smart cities [55], improving cloud gaming experience with short delay [56], designing energy monitoring systems in smart homes [57], or maximizing social welfare of live video streaming services [58]. Moreover, the digital twins that leverage the IoT devices and network structure have a broad application scope regarding the six typical scenarios, for example, creating a virtual representation of the healthcare sector [59], tracking student-centered gaze patterns without instrumenting the student [60], improving smart city construction and urban management [61], virtual tours and online shopping [62], urban planning and city administration [63], and social digital twins with virtualreal interactions [64].…”

Section: B Key Contributions Of This Surveymentioning

confidence: 99%

When Internet of Things Meets Metaverse: Convergence of Physical and Cyber Worlds

Cui

et al. 2023

IEEE Internet Things J.

105

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14In recent years, the Internet of Things (IoT) is studied in the context of the Metaverse to provide users immersive cyber-virtual experiences in mixed reality environments. This survey introduces six typical IoT applications in the Metaverse, including collaborative healthcare, education, smart city, entertainment, real estate, and socialization. In the IoT-inspired Metaverse, we also comprehensively survey four pillar technologies that enable augmented reality (AR) and virtual reality (VR), namely, responsible artificial intelligence (AI), highspeed data communications, cost-effective mobile edge computing (MEC), and digital twins. According to the physical-world demands, we outline the current industrial efforts and seven key requirements for building the IoTinspired Metaverse: immersion, variety, economy, civility, interactivity, authenticity, and independence. In addition, this survey describes the open issues in the IoT-inspired Metaverse, which need to be addressed to eventually achieve the convergence of physical and cyber worlds.

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“…Notwithstanding this, the DT is proliferating other domains such as space cooling/heating [ 28 , 29 ], wind turbine fault diagnosis [ 30 , 31 , 32 ], bio-processes [ 33 , 34 ], energy [ 35 ], oil and gas [ 36 ], automotive [ 37 , 38 , 39 ], infrastructure [ 40 , 41 ], and defense [ 42 ] to name a few. More recently, DT concepts are being proposed in healthcare and medicine and are expected to provide significant advantages [ 43 , 44 ]. Mainly the DT is expected to personalize treatment pathways.…”

Section: Introductionmentioning

confidence: 99%

Human Digital Twin for Personalized Elderly Type 2 Diabetes Management

Padmapritha

Srinivasan

Kesavadev

et al. 2023

JCM

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Managing Elderly type 2 diabetes (E-T2D) is challenging due to geriatric conditions (e.g., co-morbidity, multiple drug intake, etc.), and personalization becomes paramount for precision medicine. This paper presents a human digital twin (HDT) framework to manage E-T2D that exploits various patient-specific data and builds a suite of models exploiting the data for prediction and management to personalize diabetes treatment in E-T2D patients. These models include mathematical and deep-learning ones that capture different patient aspects. Consequently, the HDT virtualizes the patient from different viewpoints using an HDT that mimics the patient and has interfaces to update the virtual models simultaneously from measurements. Using these models the HDT obtains deeper insights about the patient. Further, an adaptive patient model fusing this information and a learning-based model predictive control (LB-MPC) algorithm are proposed. The geriatric conditions are captured as model parameters and constraints while solving the LB-MPC to personalize the insulin infusion for E-T2D management. The HDT is deployed on and illustrated with 15 patients using clinical trials and simulations. Our results show that HDT helps improve the time-in-range from 3–75% to 86–97% and reduces insulin infusion by 14–29%.

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Digital Twins for Healthcare 4.0—Recent Advances, Architecture, and Open Challenges

Cited by 56 publications

References 14 publications

Metaverse for Healthcare: A Survey on Potential Applications, Challenges and Future Directions

Metaverse for Healthcare: A Survey on Potential Applications, Challenges and Future Directions

When Internet of Things Meets Metaverse: Convergence of Physical and Cyber Worlds

Human Digital Twin for Personalized Elderly Type 2 Diabetes Management

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