Improving Clinical Diagnostics and Patient Care through Artificial Intelligence and Biosensor Technologies

This perspective analyzes the substantial advantages of Artificial Intelligence (AI) and machine learning (ML) in improving the efficacy and precision of biosensors, facilitating accurate detection of diverse physiological signals. Moreover, it emphasizes contemporary developments in biosensor technology and their uses in medical diagnosis, stressing their ability for early disease detection and continuous monitoring. The study also addresses major barriers to more widespread use, such as the lack of high-quality data sets, data variability issues, and the restricted relevance of many artificial intelligence techniques. Ethical questions about data privacy and security are also addressed, as are legal difficulties resulting from the rapid technological development of artificial intelligence. By exploring innovative approaches to overcome challenges, this study emphasizes the possibility of AI-enhanced biosensing systems to significantly improve healthcare results and support individualized medicine.

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Publication Details

Journal
ACS Omega
Published
2026-01-02
DOI
https://doi.org/10.1021/acsomega.5c06072
Citations
1
Primary Topic
Artificial Intelligence in Healthcare and Education
Type
article
Field-Weighted Citation Impact
17.70
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article

Improving Clinical Diagnostics and Patient Care through Artificial Intelligence and Biosensor Technologies

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1 citations
ACS Omega
Artificial Intelligence in Healthcare and Education
17.70
article

Improving Clinical Diagnostics and Patient Care through Artificial Intelligence and Biosensor Technologies

Vishnu Suresh, Lien Smeesters, Joanna Cabaj, Sylwia Baluta, Milena Chmielowska
article en
1 citations

Abstract

This perspective analyzes the substantial advantages of Artificial Intelligence (AI) and machine learning (ML) in improving the efficacy and precision of biosensors, facilitating accurate detection of diverse physiological signals. Moreover, it emphasizes contemporary developments in biosensor technology and their uses in medical diagnosis, stressing their ability for early disease detection and continuous monitoring. The study also addresses major barriers to more widespread use, such as the lack of high-quality data sets, data variability issues, and the restricted relevance of many artificial intelligence techniques. Ethical questions about data privacy and security are also addressed, as are legal difficulties resulting from the rapid technological development of artificial intelligence. By exploring innovative approaches to overcome challenges, this study emphasizes the possibility of AI-enhanced biosensing systems to significantly improve healthcare results and support individualized medicine.

ACS OmegaVol. 11(1)
Wrocław University of Science and Technology (PL), Vrije Universiteit Brussel (BE), Łukasiewicz Research Network - Industrial Chemistry Institute (PL), AGH University of Krakow (PL)
Openalex Percentile: Top 4%
Artificial Intelligence in Healthcare and Education
17.70
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