Evidence map›Paper›PMID 33002894›Full record

ReviewCurrent opinion in allergy and clinical immunology2020

Artificial intelligence and the hunt for immunological disorders.

Nicholas L Rider, Renganathan Srinivasan, Paneez Khoury

Abstract readReview
PubMed Full text at PMC Publisher
In one paragraph

Review in Current opinion in allergy and clinical immunology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.

0numbers the graph read from it
0cells of the map it votes in
13citing 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

13 citing papers in PubMed.

  1. Review
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  6. Article
  7. Review
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  9. Review
  10. Article
  11. Computer-Assisted Clinical Diagnosis and Treatment.Current allergy and asthma reports · 2023
    Review
  12. Review
  13. Article
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.

Nicholas L RiderTexas Children's Hospital and the Baylor College of Medicine, Houston, Texas.
Renganathan SrinivasanThe Vancouver Clinic, Vancouver, Washington.
Paneez KhouryLaboratory of Parasitic Diseases, National Institute of Allergic and Infectious Diseases, NIH, Bethesda, Maryland, USA.

Funding

Intramural NIH HHS Z99 AI999999
6 · The paper itself

Abstract

purpose of reviewArtificial intelligence has pervasively transformed many industries and is beginning to shape medical practice. New use cases are being identified in subspecialty domains of medicine and, in particular, application of artificial intelligence has found its way to the practice of allergy-immunology. Here, we summarize recent developments, emerging applications and obstacles to realizing full potential. RECENT

findingsArtificial/augmented intelligence and machine learning are being used to reduce dimensional complexity, understand cellular interactions and advance vaccine work in the basic sciences. In genomics, bioinformatic methods are critical for variant calling and classification. For clinical work, artificial intelligence is enabling disease detection, risk profiling and decision support. These approaches are just beginning to have impact upon the field of clinical immunology and much opportunity exists for further advancement. SUMMARY: This review highlights use of computational methods for analysis of large datasets across the spectrum of research and clinical care for patients with immunological disorders. Here, we discuss how big data methods are presently being used across the field clinical immunology.

Indexed as

Artificial IntelligenceCell CommunicationClinical Decision-MakingComputational BiologyDatasets as TopicHumansImmune System DiseasesMachine LearningVaccinesVaccines

Identifiers

PMID33002894
PMCPMC7908683

What OpenQuestion holds

Textmetadata
LicenceTDM
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.