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Digital Hybrid Model Preparation for Virtual Planning of Reconstructive Dentoalveolar Surgical Procedures

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Structural Topic Modeling Analysis of Digital Twin Study in Healthcare.

Yooseok Lim¹, Eun Man Kim²

¹Department of Industrial and Information Systems Engineering, Soongsil University.

Studies in Health Technology and Informatics

|August 8, 2025

Summary

This summary is machine-generated.

This study used Structural Topic Modeling to analyze digital twin (DT) research in healthcare, identifying eight key topics. These findings highlight DT

Keywords:

Digital twin Healthcare Structural Topic Modeling

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Area of Science:

Health Informatics
Computer Science Applications in Medicine

Background:

Digital Twin (DT) technology is increasingly integrated into various sectors.
Healthcare is exploring advanced technologies for improved efficiency and personalization.

Purpose of the Study:

To identify and categorize the main research topics concerning Digital Twins (DT) in the healthcare domain.
To provide a structured overview of the current landscape of DT applications in healthcare.

Main Methods:

A systematic literature search was conducted across five databases from 2018 to 2024.
Structural Topic Modeling (STM) was employed to analyze the gathered research papers.
Topic selection was based on semantic coherence, lower bound, and held-out likelihood metrics.

Main Results:

Eight distinct topics emerged from the analysis of the digital twin in healthcare literature.
The study identified key areas of focus and emerging trends in DT research within healthcare.

Conclusions:

Digital Twin technology is a significant driver of innovation in healthcare.
The integration of DTs promises more efficient, personalized, and advanced healthcare solutions.