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

Bo Sheng; Zheyu Wang; Yujiao Qiao; Sheng Quan Xie; Jing Tao; Chaoqun Duan

doi:10.1177/20552076231203672

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

Digit Health. 2023 Oct 12:9:20552076231203672. doi: 10.1177/20552076231203672. eCollection 2023 Jan-Dec.

Authors

Bo Sheng^{1

2}, Zheyu Wang¹, Yujiao Qiao³, Sheng Quan Xie⁴, Jing Tao¹, Chaoqun Duan¹

Affiliations

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.

Keywords: Healthcare; artificial intelligence; digital twin; structure topic modeling; text data mining.

Publication types

Review