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Review
. 2024 May 25;14(11):1100.
doi: 10.3390/diagnostics14111100.

Artificial Intelligence in Point-of-Care Biosensing: Challenges and Opportunities

Connor D Flynn  1 , Dingran Chang  2
Affiliations
Review

Artificial Intelligence in Point-of-Care Biosensing: Challenges and Opportunities

Connor D Flynn et al. Diagnostics (Basel). .

Abstract

The integration of artificial intelligence (AI) into point-of-care (POC) biosensing has the potential to revolutionize diagnostic methodologies by offering rapid, accurate, and accessible health assessment directly at the patient level. This review paper explores the transformative impact of AI technologies on POC biosensing, emphasizing recent computational advancements, ongoing challenges, and future prospects in the field. We provide an overview of core biosensing technologies and their use at the POC, highlighting ongoing issues and challenges that may be solved with AI. We follow with an overview of AI methodologies that can be applied to biosensing, including machine learning algorithms, neural networks, and data processing frameworks that facilitate real-time analytical decision-making. We explore the applications of AI at each stage of the biosensor development process, highlighting the diverse opportunities beyond simple data analysis procedures. We include a thorough analysis of outstanding challenges in the field of AI-assisted biosensing, focusing on the technical and ethical challenges regarding the widespread adoption of these technologies, such as data security, algorithmic bias, and regulatory compliance. Through this review, we aim to emphasize the role of AI in advancing POC biosensing and inform researchers, clinicians, and policymakers about the potential of these technologies in reshaping global healthcare landscapes.

Keywords: artificial intelligence; biosensor; machine learning; point-of-care.

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Conflict of interest statement

The authors declare no conflicts of interest.

Figures

Figure 1
Figure 1
The biosensor development process.
Figure 2
Figure 2
Commercial point-of-care biosensors for at-home use.
Figure 3
Figure 3
Overview of artificial intelligence approaches.
Figure 4
Figure 4
Artificial intelligence approaches for analyte discovery and understanding.
Figure 5
Figure 5
Artificial intelligence approaches to recognition element discovery.
Figure 6
Figure 6
Artificial intelligence approaches to biosensor transduction.
Figure 7
Figure 7
Artificial intelligence approaches for biosensor data handling.
Figure 8
Figure 8
General workflow for artificial-intelligence-assisted model development.
See this image and copyright information in PMC

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