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Computational Insights Into Smart Bioelectronics In Digital Health Care (2020-2024): Topic Modeling Study.

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Computational Insights Into Smart Bioelectronics In Digital Health Care (2020-2024): Topic Modeling Study.

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Dynamic Digital Biomarkers of Motor and Cognitive Function in Parkinson's Disease

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Published on: July 24, 2019

Computational Insights Into Smart Bioelectronics in Digital Health Care (2020-2024): Topic Modeling Study.

JiWon Bae¹, JiHoon Lee¹, Pildong Hwang¹

¹Konyang Medical Data Research Group-KYMERA, Konyang University Hospital, 158 Gwanjeodong-ro, Seo-gu, Daejeon, Republic of Korea, 82 426008679.

JMIR Medical Informatics

|June 23, 2026

View abstract on PubMed

Summary

This summary is machine-generated.

Smart bioelectronics, combining AI and hardware, are advancing digital healthcare. Research shows a shift towards AI-driven analysis and disease applications, evolving into adaptive therapeutic systems.

Keywords:

PubMed keyword analysis research trends smart bioelectronics topic modeling

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Dynamic Digital Biomarkers of Motor and Cognitive Function in Parkinson's Disease

Dynamic Digital Biomarkers of Motor and Cognitive Function in Parkinson's Disease

Published on: July 24, 2019

Area of Science:

Smart bioelectronics
Digital Health
Artificial Intelligence

Background:

Smart bioelectronics integrate hardware and AI software for analyzing bio-signals and delivering therapeutic stimulation.
These devices represent a convergence of medical technology and artificial intelligence.

Purpose of the Study:

To systematically analyze research trends in smart bioelectronics from 2020-2024.
To understand the evolving role of smart bioelectronics in digital healthcare.
To provide insights for future research and development strategies.

Main Methods:

Analysis of 92 PubMed-indexed publications (2020-2024).
Application of Latent Dirichlet allocation-based topic modeling.
Optimization of topic modeling using coherence scores to identify research themes.

Main Results:

A steady increase in smart bioelectronics research over the past 5 years.
A shift in focus from materials to AI-driven analysis and disease-oriented applications.
Key research areas: bio-signal neuromodulation (25%), AI in neurological disease analysis (34.7%), and implantable bioelectronics/biomaterials (40.2%).

Conclusions:

Smart bioelectronics are evolving into intelligent, adaptive therapeutic systems.
Insights into multidisciplinary development and potential applications in clinical decision support and personalized rehabilitation.
Highlights potential for next-generation medical device innovation.