Machine learning for bioelectronics on wearable and implantable devices: challenges and potential

Bioelectronics presents a promising future in the field of embedded and implantable electronics, providing a range of functional applications, from personal health monitoring to bioactuators. However, due to the intrinsic difficulties present in producing and optimising bioelectronics, recent research has focused on utilising Machine Learning to reliably mitigate such issues and aid in process development. This review focuses on the recent developments of integrating Machine Learning into bioelectronics, aiding in a multitude of areas such as: material development, fabrication process optimisation and system integration. First, discussing how Machine Learning has aided in the materials development by identifying complex relationships between process input parameters and desired outputs, such as product design. Second, examine the advancements in Machine Learning to accurately optimise fabrication precision and stability for various 3D printing technologies. Third, provide an overview of how Machine Learning can greatly assist in the analysis of complex, non-linear relationships in data obtained from bioelectronics. Lastly, a summary of the challenges present with utilising Machine Learning with bioelectronics and any other developments in this field. Such advancements in the field of bioelectronics and Machine Learning could hopefully build a strong foundation for this research field, promoting smart optimisation together with effective use of Machine Learning to further enhance the effectiveness of such applications.