The DOI matches its Crossref record; 159 of the 159 references it cites were found in a registry or an index.
Artificial intelligence-powered electronic skin
Open accessAbstract
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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“its application in electrocardiogram (ECG) diagnosis is particularly well suited27,28.”
From Flexible self-rectifying synapse array for energy-efficient edge multiplication in electrocardiogram diagnosis (2025) · cites this paper as [28]
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Toward an AI Era: Advances in Electronic Skins
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Metadata
- Authors
- Xu, C., Solomon, S. A., & Gao, W.
- Affiliations
- California Institute of Technology (US)
- Corresponding author
- Gao, Wei
- Venue
- Nature Machine Intelligence
- Published
- 2023-12-18
- Volume
- 5
- Issue
- 12
- Pages
- 1344-1355
- ISSN
- 2522-5839 (ISSN-L)
- 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
- 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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