Metaverse and Healthcare: Machine Learning-Enabled Digital Twins of Cancer

Aug 5, 2026

Medical digital twins bridge the physical world and the metaverse, supporting patients in accessing virtual medical services and enabling immersive interactions. Cancer is highlighted as a major disease that can be diagnosed and treated through this technology, though disease digitalization remains highly complex. The study uses machine learning to build real-time, reliable cancer digital twins for diagnostic and therapeutic aims, emphasizing four classical methods suited to medical specialists and to IoMT constraints on latency and cost, with a breast cancer case study and a conceptual creation framework for monitoring, diagnosing, and prediction.

LanguageEnglish

CategoryHealthcare

FormatPDF

Pages23

Reading Time58 min

AccessFree to download

Introduction

Machine Learning in Cancer Digital Twins

Digital Twins and Cyber-Physical Systems

Metaverse for Healthcare Integration

bioengineering Article Metaverse and Healthcare: Machine Learning-Enabled Digital Twins of Cancer Omid Moztarzadeh 1,2, Mohammad (Behdad) Jamshidi 3, *, Saleh Sargolzaei 4, Alireza Jamshidi 5, Nasimeh Baghalipour 1, Mona Malekzadeh Moghani 6 and Lukas Hauer 1 Citation: Moztarzadeh, O.; Jamshidi, M.; Sargolzaei, S.; Jamshidi, A.; Baghalipour, N.; Malekzadeh Moghani, M.; Hauer, L. Metaverse and Healthcare: Machine Learning-Enabled Digital Twins of Cancer. Bioengineering 2023, 10, 455 . [https://doi.org/10.3390/](https://doi.org/10.3390/)[ ](https://doi.org/10.3390/)bioengineering10040455 Academic Editors: Francesca Raganati and Alessandra Procentese Received: 3 March 2023 Revised: 26 March 2023 Accepted: 5 April 2023 Published: 7 April 2023 Copyright: © 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([https://](https://)[ ](https://)[creativecommons.org/licenses/by/](creativecommons.org/licenses/by/)[ ](creativecommons.org/licenses/by/)[4.0/](4.0/)) . 1 Department of Stomatology, University Hospital Pilsen, Faculty of Medicine in Pilsen, Charles University, 32300 Pilsen, Czech Republic 2 Department of Anatomy, Faculty of Medicine in Pilsen, Charles University, 32300 Pilsen, Czech Republic 3 Faculty of Electrical Engineering, University of West Bohemia, 30100 Pilsen, Czech Republic 4 Department of Computer Engineering, Mashhad Branch, Islamic Azad University, Mashhad 9187147578, Iran 5 Dentistry School, Babol University of Medical Sciences, Babol 4717647745, Iran 6 Department of Radiation Oncology, Medical School, Shahid Beheshti, University of Medical Sciences, Teheran 1985717443, Iran * [Correspondence: bmj.jmd@gmail.com](Correspondence: bmj.jmd@gmail.com) Abstract: Medical digital twins, which represent medical assets, play a crucial role in connecting the physical world to the metaverse, enabling patients to access virtual medical services and experience immersive interactions with the real world. One serious disease that can be diagnosed and treated using this technology is cancer. However, the digitalization of such diseases for use in the metaverse is a highly complex process. To address this, this study aims to use machine learning (ML) techniques to create real-time and reliable digital twins of cancer for diagnostic and therapeutic purposes. The study focuses on four classical ML techniques that are simple and fast for medical specialists without extensive Artiﬁcial Intelligence (AI) knowledge, and meet the requirements of the Internet of Medical Things (IoMT) in terms of latency and cost. The case study focuses on breast cancer (BC), the second most prevalent form of cancer worldwide. The study also presents a comprehensive conceptual framework to illustrate the process of creating digital twins of cancer, and demonstrates the feasibility and reliability of these digital twins in monitoring, diagnosing, and predicting medical parameters. Keywords: breast cancer; digital twins; cancer; machine learning; artiﬁcial intelligence; metaverse; healthcare 1. Introduction In addition to all common methods in diagnosing and treating cancer, new methods are adapted to increase the cumulative progress of treatment plans and procedures [1,2] . Cancer digital twins are a recent method that processes input data by a variety of AI techniques and biological methods and accordingly represents precise therapy protocols [3,4] . ML methods are considered signiﬁcant analytical techniques since authentic and reliable results are obtained, whereas profound learning about input data is not required [5–7] . While Deep Learning (DL) methods have recently performed major roles during pandemic management, the demand for these methods has been proved in the medical system. Among different analytical systems, intelligence techniques are considered simple whilst they

FAQs

What is the purpose of medical digital twins in metaverse-based healthcare?

They connect the physical world to the metaverse by representing medical assets, enabling patients to access virtual services and experience immersive interactions.

How does the study propose creating digital twins of cancer?

It uses machine learning techniques to produce real-time and reliable digital twins for diagnostic and therapeutic purposes, including a breast cancer case study.

Why does the study focus on classical machine learning techniques?

They are simple and fast, can be used by medical specialists without extensive AI knowledge, and satisfy Internet of Medical Things (IoMT) requirements for latency and cost.

Aug 5, 2026

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