Evidence map›Paper›PMID 35300959›Full record

ArticleThe journal of allergy and clinical immunology. In practice2022

A Framework for Augmented Intelligence in Allergy and Immunology Practice and Research-A Work Group Report of the AAAAI Health Informatics, Technology, and Education Committee.

Paneez Khoury, Renganathan Srinivasan, Sujani Kakumanu, Sebastian Ochoa, Anjeni Keswani, Rachel Sparks, Nicholas L Rider

Open access · bronzeAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in The journal of allergy and clinical immunology. In practice, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
21citing papers in PubMed, 1 pooled it
13.1field-weighted citation impact, top 1% of its field
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

21 citing papers in PubMed, 1 synthesis or guideline pooled it, 51 citations in OpenAlex.

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  18. Computer-Assisted Clinical Diagnosis and Treatment.Current allergy and asthma reports · 2023
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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

7 authors at 5 institutions in 1 country.

Paneez KhouryLaboratory of Allergic Diseases, NIAID, Bethesda, Md. Electronic address: khouryp@nih.gov.
Renganathan SrinivasanThe Vancouver Clinic, Vancouver, Wash.
Sujani KakumanuDivision of Allergy, Pulmonary and Critical Care Medicine, University of Wisconsin School of Medicine and Public Health and the William S. Middleton Veterans Memorial Hospital, Madison, Wisc.
Sebastian OchoaNational Institutes of Infectious and Allergic Diseases, Bethesda, Md; Laboratory of Clinical Immunology and Microbiology, NIAID, Bethesda, Md.
Anjeni KeswaniDivision of Allergy/Immunology, George Washington University School of Medicine and Health Sciences, Washington, DC.
Rachel SparksNational Institutes of Infectious and Allergic Diseases, Bethesda, Md; Laboratory of Immune System Biology, NIAID, Bethesda, Md.
Nicholas L RiderSection of Immunology, Allergy and Retrovirology and the William T. Shearer Center for Human Immunobiology, Baylor College of Medicine, Texas Children's Hospital, Houston, Texas.
National Institutes of Health · USBaylor College of Medicine · USGeorge Washington University · USVancouver Clinic · USWilliam S. Middleton Memorial Veterans Hospital · US

Funding

Allergy and Immunology Clinical Fellowship Program SupportZIEAI001148 · NIAID · NATIONAL INSTITUTE OF ALLERGY AND INFECTIOUS DISEASES · PI KHOURY, PANEEZ · 2011 to 2025
$10.9M
Intramural NIH HHS ZIE AI001148
6 · The paper itself

Abstract

Artificial and augmented intelligence (AI) and machine learning (ML) methods are expanding into the health care space. Big data are increasingly used in patient care applications, diagnostics, and treatment decisions in allergy and immunology. How these technologies will be evaluated, approved, and assessed for their impact is an important consideration for researchers and practitioners alike. With the potential of ML, deep learning, natural language processing, and other assistive methods to redefine health care usage, a scaffold for the impact of AI technology on research and patient care in allergy and immunology is needed. An American Academy of Asthma Allergy and Immunology Health Information Technology and Education subcommittee workgroup was convened to perform a scoping review of AI within health care as well as the specialty of allergy and immunology to address impacts on allergy and immunology practice and research as well as potential challenges including education, AI governance, ethical and equity considerations, and potential opportunities for the specialty. There are numerous potential clinical applications of AI in allergy and immunology that range from disease diagnosis to multidimensional data reduction in electronic health records or immunologic datasets. For appropriate application and interpretation of AI, specialists should be involved in the design, validation, and implementation of AI in allergy and immunology. Challenges include incorporation of data science and bioinformatics into training of future allergists-immunologists.

Indexed as

HypersensitivityMedical InformaticsHumansIntelligenceNatural Language ProcessingTechnologyArtificial intelligenceAsthmaAtopic dermatitisAugmented intelligenceClinical decision supportElectronic health recordsEquityMachine learningMedical educationNatural language processingPrimary immunodeficiency

Identifiers

PMID35300959
PMCPMC9205719
OpenAlexW4220952043

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.