Evidence map›Paper›PMID 37080740›Full record

ReviewAnnals of laboratory medicine2023

Artificial Intelligence in Point-of-Care Testing.

Adil I Khan, Mazeeya Khan, Raheeb Khan

Abstract readReview
In one paragraph

Review in Annals of laboratory medicine, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.

0numbers the graph read from it
0cells of the map it votes in
17citing 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

17 citing papers in PubMed.

  1. Article
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  10. Patient-centred newer testing methods for major cardiac disorders.Indian journal of thoracic and cardiovascular surgery · 2025
    Review
  11. Review
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  13. Article
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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

3 authors.

Adil I KhanDepartment of Pathology and Laboratory Medicine, Lewis Katz School of Medicine, Temple University, Philadelphia, USA.ORCID 0009-0005-8141-1403
Mazeeya KhanDepartment of Infection Biology, Faculty of Infectious and Tropical Diseases, London School of Hygiene and Tropical Medicine, London, UK.ORCID 0009-0001-6564-4232
Raheeb KhanDepartment of Pathology and Laboratory Medicine, Lewis Katz School of Medicine, Temple University, Philadelphia, USA.ORCID 0009-0005-3264-1851

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

With the projected increase in the global population, current healthcare delivery models will face severe challenges. Rural and remote areas, whether in developed or developing countries, are characterized by the same challenges: the unavailability of hospitals, lack of trained and skilled staff performing tests, and poor compliance with quality assurance protocols. Point-of-care testing using artificial intelligence (AI) is poised to be able to address these challenges. In this review, we highlight some key areas of application of AI in point-of-care testing, including lateral flow immunoassays, bright-field microscopy, and hematology, demonstrating this rapidly expanding field of laboratory medicine.

Indexed as

Artificial IntelligencePoint-of-Care TestingHospitalsHumansMicroscopyAnemiaArtificial intelligenceBright-field microscopyConvolutional neural networkHematologyHemoglobinImmunoassayLateral flow immunoassayMalariaMicroscopyPoint-of-care testing

Identifiers

PMID37080740
PMCPMC10151281

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
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 OpenQuestion graph.