Machine learning in point-of-care testing: innovations, challenges, and opportunities

Gyeo-Re Han, Artem Goncharov, Merve Eryilmaz, Shun Ye, Barath Palanisamy, Rajesh Ghosh, Fabio Lisi, Elliott Rogers, David Guzman, Defne Yigci, Savas Tasoglu, Dino Di Carlo, Keisuke Goda, Rachel A. McKendry and Aydogan Ozcan ()
Additional contact information
Gyeo-Re Han: University of California
Artem Goncharov: University of California
Merve Eryilmaz: University of California
Shun Ye: University of California
Barath Palanisamy: University of California
Rajesh Ghosh: University of California
Fabio Lisi: The University of Tokyo
Elliott Rogers: University College London
David Guzman: University College London
Defne Yigci: Koç University
Savas Tasoglu: Koç University
Dino Di Carlo: University of California
Keisuke Goda: The University of Tokyo
Rachel A. McKendry: University College London
Aydogan Ozcan: University of California

Nature Communications, 2025, vol. 16, issue 1, 1-33

Abstract: Abstract The landscape of diagnostic testing is undergoing a significant transformation, driven by the integration of artificial intelligence (AI) and machine learning (ML) into decentralized, rapid, and accessible sensor platforms for point-of-care testing (POCT). The COVID-19 pandemic has accelerated the shift from centralized laboratory testing but also catalyzed the development of next-generation POCT platforms that leverage ML to enhance the accuracy, sensitivity, and overall efficiency of point-of-care sensors. This Perspective explores how ML is being embedded into various POCT modalities, including lateral flow assays, vertical flow assays, nucleic acid amplification tests, and imaging-based sensors, illustrating their impact through different applications. We also discuss several challenges, such as regulatory hurdles, reliability, and privacy concerns, that must be overcome for the widespread adoption of ML-enhanced POCT in clinical settings and provide a comprehensive overview of the current state of ML-driven POCT technologies, highlighting their potential impact in the future of healthcare.

Date: 2025
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.nature.com/articles/s41467-025-58527-6 Abstract (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:nat:natcom:v:16:y:2025:i:1:d:10.1038_s41467-025-58527-6

Ordering information: This journal article can be ordered from
https://www.nature.com/ncomms/

DOI: 10.1038/s41467-025-58527-6

Access Statistics for this article

Nature Communications is currently edited by Nathalie Le Bot, Enda Bergin and Fiona Gillespie

More articles in Nature Communications from Nature
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().