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

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.