The DOI matches its Crossref record; 158 of the 158 references it cites were found in a registry or an index.
Artificial intelligence biosensors: Challenges and prospects
Abstract
Artificial intelligence (AI) and wearable sensors are two essential fields to realize the goal of tailoring the best precision medicine treatment for individual patients. Integration of these two fields enables better acquisition of patient data and improved design of wearable sensors for monitoring the wearers' health, fitness and their surroundings. Currently, as the Internet of Things (IoT), big data and big health move from concept to implementation, AI-biosensors with appropriate technical characteristics are facing new opportunities and challenges. In this paper, the most advanced progress made in the key phases for future wearable and implantable technology from biosensing, wearable biosensing to AI-biosensing is summarized. Without a doubt, material innovation, biorecognition element, signal acquisition and transportation, data processing and intelligence decision system are the most important parts, which are the main focus of the discussion. The challenges and opportunities of AI-biosensors moving forward toward future medicine devices are also discussed.
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“One of the most significant advantages of AI-based wearable health technology is to promote preventive healthcare.”
From The emergence of AI-based wearable sensors for digital health technology: a review (2023) · cites this paper as [13]
In the citing paper
“These innovative devices can continuously track various health parameters, giving users personalized insights and feedback [6,12,13,16,188].”
From The emergence of AI-based wearable sensors for digital health technology: a review (2023) · cites this paper as [13]
Citations
Cites
Roll-to-roll production of 30-inch graphene films for transparent electrodes
Bae, S., Kim, H., Lee, Y., Xu, X., Park, J.-S., Zheng, Y., Balakrishnan, J., Lei, T., Kim, H. R., Song, Y. I., et al. (2010) · Nature Nanotechnology · doi:10.1038/nnano.2010.132 · Cited by 8,238
Fully integrated wearable sensor arrays for multiplexed in situ perspiration analysis
Gao, W., Emaminejad, S., Nyein, H. Y. Y., Challa, S., Chen, K., Peck, A., Fahad, H. M., Ota, H., Shiraki, H., Kiriya, D., et al. (2016) · Nature · doi:10.1038/nature16521 · Cited by 5,087
ELECTRODE SYSTEMS FOR CONTINUOUS MONITORING IN CARDIOVASCULAR SURGERY
Clark, L. C., & Lyons, C. (1962) · Annals of the New York Academy of Sciences · doi:10.1111/j.1749-6632.1962.tb13623.x · Cited by 3,945
Wearable biosensors for healthcare monitoring
Kim, J., Campbell, A. S., de Ávila, B. E.-F., & Wang, J. (2019) · Nature Biotechnology · doi:10.1038/s41587-019-0045-y · Cited by 3,373
Machine Learning Applications in Cancer Prognosis and Prediction
Kourou, K., Exarchos, T. P., Exarchos, K. P., Karamouzis, M. V., & Fotiadis, D. I. (2015) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2014.11.005 · Cited by 3,338
A few useful things to know about machine learning
Domingos, P. (2012) · Communications of the ACM · doi:10.1145/2347736.2347755 · Cited by 3,326
Electrochemical Biosensors - Sensor Principles and Architectures
Grieshaber, D., MacKenzie, R., Vörös, J., & Reimhult, E. (2008) · Sensors · doi:10.3390/s80314000 · Cited by 2,329
A review of saliva: normal composition, flow, and function.
Humphrey, S. P., & Williamson, R. (2001) · The Journal of prosthetic dentistry (Print) · doi:10.1067/mpr.2001.113778 · Cited by 2,276
Drummond, T. G., Hill, M. G., & Barton, J. K. (2003) · Nature Biotechnology · doi:10.1038/nbt873 · Cited by 2,214
Progress in triboelectric nanogenerators as a new energy technology and self-powered sensors
Wang, Z. L., Chen, J., & Lin, L. (2015) · Energy & Environmental Science · doi:10.1039/c5ee01532d · Cited by 2,174
Research works printed but not linked: 9
- Bohunicky, 2010, Nanotechnol. Sci. Appl., 4, 1
- Jeong, 2017, Conf. Proc. IEEE Eng. Med. Biol. Soc., 4094
- Katsuhiko, 2019, AIP Adv., 9
- Lee, 2017, Sci. Adv., 3
- Lei, 2015, Rev. Sci. Instrum., 86
- Tseng, 2018, Sensors, 18, 1, 10.1109/JSEN.2018.2870221 · DOI
- Wang, 2019, Adv. Mater., 31
- Zhang, 2017, Bull. Chin. Acad. Sci., 32, 1271
- Zhang, 2019, Adv. Mater., 31
Cited by
Sorted by citations
Technology Roadmap for Flexible Sensors
Luo, Y., Abidian, M. R., Ahn, J., Akinwande, D., Andrews, A. M., Antonietti, M., Bao, Z., Magnus Berggren, A., Berkey, C. A., Bettinger, C. J., et al. (2023) · ACS Nano · doi:10.1021/acsnano.2c12606 · Cited by 1,453
Manickam, P., Mariappan, S. A., Murugesan, S. M., Hansda, S., Kaushik, A. K., Shinde, R. B., & Thipperudraswamy, S. P. (2022) · Biosensors · doi:10.3390/bios12080562 · Cited by 717
Wearable chemical sensors for biomarker discovery in the omics era
Sempionatto, J. R., Lasalde-Ramírez, J. A., Mahato, K., Wang, J., & Gao, W. (2022) · Nature Reviews Chemistry · doi:10.1038/s41570-022-00439-w · Cited by 613
Biosensors applications in medical field: A brief review
Haleem, A., Javaid, M., Singh, R. P., Suman, R., & Rab, S. (2021) · Sensors International · doi:10.1016/j.sintl.2021.100100 · Cited by 638
The emergence of AI-based wearable sensors for digital health technology: a review
Shajari, S., Kuruvinashetti, K., Komeili, A., & Sundararaj, U. (2023) · Sensors · doi:10.3390/s23239498 · Cited by 535
Metadata
- Authors
- Jin, X., Liu, C., Xu, T., Su, L., & Zhang, X.
- Affiliations
- Shenzhen University (CN)
- Corresponding author
- Xu, Tailin
- Venue
- Biosensors and Bioelectronics
- Publisher
- Elsevier BV
- Published
- 2020-10
- Volume
- 165
- Pages
- 112412
- ISSN
- 0956-5663 (ISSN-L)
- OpenAlex
- W3039593752
- Cited by
- 399
- References
- 158
- Citations by year
- 2026: 54 · 2025: 108 · 2024: 97 · 2023: 54 · 2022: 46 · 2021: 34 · 2020: 5
- FWCI
- 2.91
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Biosensors and Bioelectronics (publishedVersion)
- PubMed (publishedVersion)
- Funders
- National Natural Science Foundation of China
- Topics
- Physical Sciences › Engineering › Biomedical Engineering › Advanced Sensor and Energy Harvesting Materials
- Physical Sciences › Chemistry › Analytical Chemistry › Electrochemical sensors and biosensors
- Physical Sciences › Engineering › Biomedical Engineering › Non-Invasive Vital Sign Monitoring
- Keywords
- wearable sensors, implantable sensors, wearable biosensing, Internet of Things, precision medicine, biorecognition elements, health monitoring, medical devices
- MeSH terms
- Artificial Intelligence, Humans, Biosensing Techniques, Precision Medicine
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