Cited by 259Advanced Sensor and Energy Harvesting MaterialsNon-Invasive Vital Sign MonitoringNanomaterials and Printing Technologies

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Artificial intelligence-powered electronic skin

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Xu, Changhao, Solomon, Samuel A., Gao, Wei·2023·Nature Machine Intelligence

DOI 10.1038/s42256-023-00760-zPMC PMC10868719

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Abstract

Skin-interfaced electronics is gradually changing medical practices by enabling continuous and noninvasive tracking of physiological and biochemical information. With the rise of big data and digital medicine, next-generation electronic skin (e-skin) will be able to use artificial intelligence (AI) to optimize its design as well as uncover user-personalized health profiles. Recent multimodal e-skin platforms have already employed machine learning (ML) algorithms for autonomous data analytics. Unfortunately, there is a lack of appropriate AI protocols and guidelines for e-skin devices, resulting in overly complex models and non-reproducible conclusions for simple applications. This review aims to present AI technologies in e-skin hardware and assess their potential for new inspired integrated platform solutions. We outline recent breakthroughs in AI strategies and their applications in engineering e-skins as well as understanding health information collected by e-skins, highlighting the transformative deployment of AI in robotics, prosthetics, virtual reality, and personalized healthcare. We also discuss the challenges and prospects of AI-powered e-skins as well as predictions for the future trajectory of smart e-skins.

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Authors: Xu, C., Solomon, S. A., & Gao, W.
Affiliations: California Institute of Technology (US)
Corresponding author: Gao, Wei
ORCID: Xu, Changhao
Solomon, Samuel A.
Gao, Wei
Venue: Nature Machine Intelligence
Publisher: Springer Science and Business Media LLC
Published: 2023-12-18
Volume: 5
Issue: 12
Pages: 1344-1355
ISSN: 2522-5839 (ISSN-L)
DOI: 10.1038/s42256-023-00760-z
PMC: PMC10868719
OpenAlex: W4389918060
Cited by: 259
References: 159
Citations by year: 2026: 80 · 2025: 110 · 2024: 67 · 2023: 2
FWCI: 20.46
Type: journal article
Language: en
Open access: green
www.ncbi.nlm.nih.gov
Licence: www.springernature.com/gp/researchers/text-and-data-mining (tdm)
www.springernature.com/gp/researchers/text-and-data-mining (vor)
Locations: Nature Machine Intelligence (publishedVersion)
PubMed (publishedVersion)
PubMed Central (acceptedVersion)
Funders: United States Department of Defense | United States Navy | Office of Naval Research (N00014-21-1-2483, N00014-21-1-2845)
United States Department of Defense | United States Army | U.S. Army Research, Development and Engineering Command | Army Research Office (W911NF-23-1-0041)
U.S. Department of Health & Human Services | National Institutes of Health (R01HL155815, R21DK13266)
National Science Foundation (2145802)
National Academies of Sciences, Engineering, and Medicine | National Academy of Medicine
Topics: Physical Sciences › Engineering › Biomedical Engineering › Advanced Sensor and Energy Harvesting Materials
Physical Sciences › Engineering › Biomedical Engineering › Non-Invasive Vital Sign Monitoring
Physical Sciences › Engineering › Electrical and Electronic Engineering › Nanomaterials and Printing Technologies
Keywords: electronic skin, artificial intelligence, flexible electronics, health monitoring, machine learning, human-machine interaction, personalized healthcare, prosthetics, robotics, virtual reality

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