Evidence map›Paper›PMID 40175414›Full record

ReviewNature communications2025

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 and 5 more

Abstract readReview
PubMed Full text at PMC Publisher
In one paragraph

Review in Nature communications, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 65 papers.

0numbers the graph read from it
0cells of the map it votes in
65citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

65 citing papers in PubMed.

  1. Article
  2. Review
  3. Review
  4. Review
  5. Review
  6. Review
  7. Review
  8. Article
  9. Article
  10. Article
  11. Article
  12. Review
  13. Article
  14. Article
  15. Review
  16. Review
  17. Managing maternity: Moving care, not patients, using artificial intelligence (AI), internet-of-things (IOT) and point-of-care testing (POCT) devices.International journal of gynaecology and obstetrics: the official organ of the International Federation of Gynaecology and Obstetrics · 2026
    Article
  18. Review
  19. Review
  20. Article

5 more citing papers are in PubMed but not listed here.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

15 authors.

Gyeo-Re Han *Electrical & Computer Engineering Department, University of California, Los Angeles, CA, USA.ORCID http://orcid.org/0000-0003-3584-4433
Artem Goncharov *Electrical & Computer Engineering Department, University of California, Los Angeles, CA, USA.
Merve EryilmazElectrical & Computer Engineering Department, University of California, Los Angeles, CA, USA.ORCID http://orcid.org/0000-0003-3034-7665
Shun YeBioengineering Department, University of California, Los Angeles, CA, USA.ORCID http://orcid.org/0000-0003-3457-9359
Barath PalanisamyBioengineering Department, University of California, Los Angeles, CA, USA.ORCID http://orcid.org/0000-0002-7117-2876
Rajesh GhoshBioengineering Department, University of California, Los Angeles, CA, USA.ORCID http://orcid.org/0000-0002-7408-8944
Fabio LisiDepartment of Chemistry, The University of Tokyo, Tokyo, Japan.ORCID http://orcid.org/0000-0003-3597-596X
Elliott RogersLondon Centre for Nanotechnology and Division of Medicine, University College London, London, UK.ORCID http://orcid.org/0000-0002-8348-8659
David GuzmanLondon Centre for Nanotechnology and Division of Medicine, University College London, London, UK.ORCID http://orcid.org/0000-0001-7005-4467
Defne YigciDepartment of Mechanical Engineering, Koç University, Istanbul, Türkiye.
Savas TasogluDepartment of Mechanical Engineering, Koç University, Istanbul, Türkiye.ORCID http://orcid.org/0000-0003-4604-217X
Dino Di CarloBioengineering Department, University of California, Los Angeles, CA, USA.ORCID http://orcid.org/0000-0003-3942-4284
Keisuke GodaDepartment of Chemistry, The University of Tokyo, Tokyo, Japan.ORCID http://orcid.org/0000-0001-6302-6038
Rachel A McKendryLondon Centre for Nanotechnology and Division of Medicine, University College London, London, UK.ORCID http://orcid.org/0000-0003-2018-6829
Aydogan OzcanElectrical & Computer Engineering Department, University of California, Los Angeles, CA, USA. ozcan@ucla.edu.ORCID http://orcid.org/0000-0002-0717-683X

Funding

National Science Foundation (NSF) 1648451
6 · The paper itself

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.

Indexed as

COVID-19Machine LearningPoint-of-Care TestingArtificial IntelligenceCOVID-19 TestingHumansPandemicsPoint-of-Care SystemsReproducibility of ResultsSARS-CoV-2

Identifiers

PMID40175414
PMCPMC11965387

What Socratic holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.