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

Author

Listed:
  • Gyeo-Re Han

    (University of California)

  • Artem Goncharov

    (University of California)

  • Merve Eryilmaz

    (University of California
    University of California)

  • Shun Ye

    (University of California
    University of California)

  • Barath Palanisamy

    (University of California
    University of California)

  • Rajesh Ghosh

    (University of California
    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
    Koç University
    Max Planck Institute for Intelligent Systems)

  • Dino Di Carlo

    (University of California
    University of California)

  • Keisuke Goda

    (The University of Tokyo)

  • Rachel A. McKendry

    (University College London)

  • Aydogan Ozcan

    (University of California
    University of California
    University of California
    University of California)

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.

Suggested Citation

  • 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 & , 2025. "Machine learning in point-of-care testing: innovations, challenges, and opportunities," Nature Communications, Nature, vol. 16(1), pages 1-33, December.
    • Handle: RePEc:nat:natcom:v:16:y:2025:i:1:d:10.1038_s41467-025-58527-6
    DOI: 10.1038/s41467-025-58527-6
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