Machine Learning for Bioelectronics on Wearable and Implantable Devices: Challenges and Potential.

Area of Science:

• Bioelectronics
• Machine Learning
• Materials Science

Background:

• Bioelectronics offers advanced applications in health monitoring and bioactuation.
• Challenges in bioelectronic production and optimization necessitate innovative solutions.
• Machine learning (ML) is emerging as a key technology to address these challenges.

Purpose of the Study:

• To review recent advancements in integrating ML with bioelectronics.
• To highlight ML's role in material development, fabrication optimization, and system integration.
• To provide insights for both bioelectronics and ML researchers.

Main Methods:

• Literature review of recent developments in ML applications for bioelectronics.
• Analysis of ML's impact on material property prediction and design.
• Examination of ML algorithms for optimizing 3D printing fabrication processes.
• Overview of ML for analyzing complex bioelectronic data.

Main Results:

• ML effectively identifies complex relationships between process parameters and bioelectronic product design.
• ML enhances precision and stability in 3D printed bioelectronic devices.
• ML facilitates the analysis of nonlinear data from bioelectronic systems.
• Identified challenges and future directions for ML in bioelectronics.

Conclusions:

• The integration of ML significantly advances bioelectronics research and development.
• ML promotes smart optimization, enhancing the effectiveness of bioelectronic applications.
• This review bridges the gap between ML and bioelectronics, fostering interdisciplinary collaboration.