Recent Advances in Hydrogel‐Based Soft Bioelectronics and its Convergence with Machine Learning

• Published in: Advanced engineering materials Vol. 26; no. 22
• Main Authors: Lee, Eun Seo, Lee, Min Young, Kim, Dae‐Hyeong, Koo, Ja Hoon
• Format: Journal Article
• Language: English
• Published: 01.11.2024
• Subjects: biointegrated device; biomedical devices; functional hydrogels; machine learning; soft bioelectronics; stretchable electronics
• ISSN: 1438-1656; 1527-2648
• DOI: 10.1002/adem.202401432

Recent advancements in artificial intelligence (AI) technologies, particularly machine learning (ML) techniques, have opened up a promising frontier in the development of intelligent soft bioelectronics, demonstrating unparalleled performance in interfacing with the human body. Hydrogels, owing to their unique combination of biocompatibility, tunable mechanical properties, and high water content, have emerged as a versatile platform for constructing soft bioelectronic devices. Functionalized hydrogels, such as conductive hydrogels, can efficiently capture biosignals from various target tissues while seamlessly forming conformal and reliable interfaces. They can also function as an intermediary layer between biological tissues and soft bioelectronics for diagnosis and therapy purposes. Meanwhile, ML has demonstrated its efficacy in processing extensive datasets collected from the bioelectronics. The convergence of hydrogel‐based soft bioelectronics and ML has unlocked a myriad of possibilities in unprecedented diagnostics, therapeutics, and beyond. In this review, the latest advances in hydrogel‐based soft bioelectronics are introduced. After briefly describing the materials and device strategies for high‐performance hydrogel bioelectronics, how ML can be integrated to augment the functionalities is discussed. Recent examples of ML‐integrated hydrogel bioelectronics are then discussed. Finally, the review is concluded by introducing future potential applications of AI in hydrogel‐based bioelectronics, alongside inherent challenges in this interdisciplinary domain. The integration of machine learning techniques with hydrogel‐based soft bioelectronics is opening a new avenue for intelligent biomedical devices and systems. The hydrogel‐based bioelectronics can achieve seamless integration with human biological tissues, thus achieving reliable and sustainable operation. This facilitates high‐quality biosignal acquisition, which, in turn, is used for training the artificial intelligence for efficient data processing and system design.