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Futuristic biosensors for cardiac health care: an artificial intelligence approach
Open accessAbstract
Biosensor-based devices are pioneering in the modern biomedical applications and will be the future of cardiac health care. The coupling of artificial intelligence (AI) for cardiac monitoring-based biosensors for the point of care (POC) diagnostics is prominently reviewed here. This review deciphers the most significant machine-learning algorithms for the futuristic biosensors along with the internet of things, computational techniques and microchip-based essential cardiac biomarkers for real-time health monitoring and improving patient compliance. The present review also discusses the recently developed cardiac biosensors along with technical strategies involved in their mechanism of working and their applications in healthcare. Additionally, it provides a key for the ontogeny of an effective and supportive hierarchical protocol for clinical decision-making about personalized medicine through combinatory information analysis, and integrated multidisciplinary AI approaches.
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“As the biosensors are still in the development stage in monitoring biochemical signals, the technology is yet to sense biological biomarkers continuously, and they need to achieve higher sensitivity, selectivity, and accuracy towards specific biomolecule sensing [14,17].”
From The emergence of AI-based wearable sensors for digital health technology: a review (2023) · cites this paper as [17]
In the citing paper
“DL algorithms have been proven effective in the early detection and preventing of chronic diseases, like diabetes and cardiovascular disease [17,195].”
From The emergence of AI-based wearable sensors for digital health technology: a review (2023) · cites this paper as [17]
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“Integration with smartphones and mobile devices enhances the accessibility of wearable ECG monitors for real-time monitoring, data storage, and cloud-based analytics, enabling seamless integration into telemedicine and remote patient monitoring systems [4,16,17].”
From The emergence of AI-based wearable sensors for digital health technology: a review (2023) · cites this paper as [17]
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Influential
Metadata
- Authors
- Vashistha, R., Dangi, A. K., Kumar, A., Chhabra, D., & Shukla, P.
- Affiliations
- Maharshi Dayanand University (IN)
- Corresponding author
- Shukla, Pratyoosh
- Venue
- 3 Biotech
- Published
- 2018-08
- Volume
- 8
- Issue
- 8
- Pages
- 358-358
- ISSN
- 2190-572X (ISSN-L)
- 2190-5738
- PMC
- PMC6081842
- OpenAlex
- W2885820762
- Cited by
- 113
- References
- 74
- Citations by year
- 2026: 6 · 2025: 15 · 2024: 18 · 2023: 17 · 2022: 19 · 2021: 12 · 2020: 18 · 2019: 8
- FWCI
- 0.77
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- 3 Biotech (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Topics
- Physical Sciences › Engineering › Biomedical Engineering › Non-Invasive Vital Sign Monitoring
- Health Sciences › Medicine › Cardiology and Cardiovascular Medicine › ECG Monitoring and Analysis
- Physical Sciences › Computer Science › Information Systems › Internet of Things and AI
- Keywords
- artificial intelligence, machine learning, point-of-care testing, cardiac monitoring, Internet of Things, cardiac biomarkers, real-time health monitoring, medication adherence, personalized medicine, clinical decision-making, wearable sensors
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