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

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.

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Publication Details

Journal
Advanced Engineering Materials
Published
2024-09-08
DOI
https://doi.org/10.1002/adem.202401432
Citations
17
Primary Topic
Advanced Sensor and Energy Harvesting Materials
Type
article
Field-Weighted Citation Impact
3.14

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article

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

Dae‐Hyeong Kim, Ja Hoon Koo, Min Young Lee, Eun Seo Lee
17 citations
Advanced Engineering Materials
Advanced Sensor and Energy Harvesting Materials
3.14
article

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

Dae‐Hyeong Kim, Ja Hoon Koo, Min Young Lee, Eun Seo Lee
article en
17 citations

Abstract

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.

Advanced Engineering MaterialsVol. 26(22)
Seoul National University (KR), Sejong University (KR), Institute for Basic Science (KR)
National Research Foundation, National Research Foundation of Korea, Ministry of Science and ICT, South Korea
Openalex Percentile: Top 8%
Advanced Sensor and Energy Harvesting Materials
3.14
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