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

Paper

Abstract

Recent trends have shown a widespread increase in the landscape of digital healthcare (i.e., Healthcare 4.0) services, such as personalized healthcare, intelligent rehabilitation, telemedicine, and smart diet management, among others. These healthcare services are based on a variety of diverse requirements. Fulfilling these requirements require proactive intelligent analytics and self-sustainability of networks. Self-sustainability enables the operation of a network with minimum possible interaction from the end-users/network operators, whereas proactive intelligent analytics enables efficient management of resources in response to users’ requests. To enable healthcare 4.0 with proactive online analytics and self-sustainability, one can leverage digital twins. In this article, we present an overview and recent advances of digital twins for healthcare 4.0. An architecture of digital twins for healthcare is also proposed. Furthermore, we present several use cases of digital twins. Finally, we present open research challenges with possible solutions.

Analysis

Paper intelligence

Summary

This paper provides an overview of digital twins for Healthcare 4.0, proposing an architecture and discussing recent advances, use cases, and open challenges.

Key findings

Digital twins can enable proactive intelligent analytics and self-sustainability in Healthcare 4.0.
Digital twins support diverse healthcare services like personalized healthcare and telemedicine.
An architecture for digital twins in healthcare is proposed.

Methods

Literature review of recent advances in digital twins for healthcare.
Proposal of a digital twin architecture for Healthcare 4.0.
Discussion of use cases and open research challenges.

Limitations

The paper focuses on recent advances and challenges, not a specific empirical study.
Specific validation of the proposed architecture is not detailed in the abstract.

Applications

Personalized healthcare
Intelligent rehabilitation
Telemedicine
Smart diet management

Confidence100%

Discovery

Related papers

2026Crossref0 citations

Healthcare Digital Twins Across Scales: A Narrative Review and Five-Level Conceptual Framework

Leonard Azamfirei; Dorin Bica; Andrei Calin Dragomir; Emoke Almasy

Aug 7, 2026Healthcare

2026Crossref0 citations

INTELLIGENT CANCER DIGITAL TWINS: AI-DRIVEN VIRTUAL PATIENT MODELS FOR PREDICTIVE ONCOLOGY

Dr. Kunal Mehta; Dr. Farah Ali

Jul 18, 2026International Journal of Advanced Research and Innovations

2026Crossref0 citations

Patient-specific digital twins in aortic disease: integrating computational hemodynamics, immune profiling, and precision endovascular strategy

Rasit Dinc; Nurittin Ardic

May 11, 2026Frontiers in Cardiovascular Medicine

2026Crossref3 citations

Virtual Brain and Digital Twins in Neurogenetics: From Multimodal Patient Data to Genomically Informed, Clinically Actionable Models

Lorenzo Cipriano

May 2, 2026Applied Biosciences

2026Crossref0 citations

Medical students’ learning experiences using a virtual patient simulator

Tiina Salmijärvi; Paula Veikkolainen; Anu Kajamaa; Hanni Muukkonen; Petri Kulmala; Jarmo Reponen

Mar 31, 2026Finnish Journal of eHealth and eWelfare

2026Crossref1 citations

Exploring Professionals’ Perceptions of the Potential of Digital Twins in Homecare—A Focus Group Study in Sweden

Sandra Saade; Susanna Nordin; Johan Borg

Jan 23, 2026Healthcare

Source record

Publication metadata

Authors: Mamoun Alazab; Latif U. Khan; Srinivas Koppu; Swarna Priya Ramu; M. Iyapparaja; Parimala Boobalan; Thar Baker; Praveen Kumar Reddy Maddikunta; Thippa Reddy Gadekallu; Ahamed Aljuhani
Published: Sep 23, 2022
Journal or venue: IEEE Consumer Electronics Magazine
DOI: 10.1109/mce.2022.3208986
Paper type: journal article
Source: Openalex

DOI OpenAlex