Detecting latent topics and trends of digital twins in healthcare: A structural topic model-based systematic review

Author:

Sheng Bo12,Wang Zheyu1,Qiao Yujiao3,Xie Sheng Quan4,Tao Jing1ORCID,Duan Chaoqun1

Affiliation:

1. School of Mechatronic Engineering and Automation, Shanghai University, Shanghai, China

2. Shanghai Key Laboratory of Intelligent Manufacturing and Robotics, Shanghai University, Shanghai, China

3. ShanghaiTech University Center for Innovative Teaching and Learning, ShanghaiTech University, Shanghai, China

4. School of Electronic and Electrical Engineering, University of Leeds, Leeds, UK

Abstract

Objective Digital twins (DTs) have received widespread attention recently, providing new ideas and possibilities for future healthcare. This review aims to provide a quantitative review to analyze specific study contents, research focus, and trends of DT in healthcare. Simultaneously, this review intends to expand the connotation of “healthcare” into two directions, namely “Disease treatment” and “Health enhancement” to analyze the content within the “DT + healthcare” field thoroughly. Methods A data mining method named Structure Topic Modeling (STM) was used as the analytical tool due to its topic analysis ability and versatility. Google Scholar, Web of Science, and China National Knowledge Infrastructure supplied the material papers in this review. Results A total of 94 high-quality papers published between 2018 and 2022 were gathered and categorized into eight topics, collectively covering the transformative impact across a broader spectrum in healthcare. Three main findings have emerged: (1) papers published in healthcare predominantly concentrate on technology development (artificial intelligence, Internet of Things, etc.) and application scenarios(personalized, precise, and real-time health service); (2) the popularity of research topics is influenced by various factors, including policies, COVID-19, and emerging technologies; and (3) the preference for topics is diverse, with a general inclination toward the attribute of “Health enhancement.” Conclusions This review underscores the significance of real-time capability and accuracy in shaping the future of DT, where algorithms and data transmission methods assume central importance in achieving these goals. Moreover, technological advancements, such as omics and Metaverse, have opened up new possibilities for DT in healthcare. These findings contribute to the existing literature by offering quantitative insights and valuable guidance to keep researchers ahead of the curve.

Funder

National Natural Science Foundation of China

Shanghai Pujiang Program

Shanghai Sports science and technology project

Publisher

SAGE Publications

Subject

Health Information Management,Computer Science Applications,Health Informatics,Health Policy

Reference108 articles.

1. Healthcare 4.0: trends, challenges and research directions

2. Grieves M. Origins of the Digital Twin Concept. Published online 2016. DOI: 10.13140/RG.2.2.26367.61609

3. Gartners Top 10 Technology Trends 2017, https://www.gartner.com/smarterwithgartner/gartners-top-10-technology-trends-2017

4. Gartner Top 10 Strategic Technology Trends for 2018. Gartner. Published 2017, https://www.gartner.com/smarterwithgartner/gartner-top-10-strategic-technology-trends-for-2018

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