Evidence map›Paper›PMID 40709193›Full record

ReviewFrontiers in immunology2025

New tools for diagnosis of primary immunodeficiencies: from awareness to artificial intelligence.

Pere Soler-Palacín, Jacques G Rivière, Siobhán O Burns, Nicholas L Rider

Abstract readReview
In one paragraph

Review in Frontiers in immunology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing 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

4 citing papers in PubMed.

  1. Review
  2. Article
  3. Article
  4. Review
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

4 authors.

Pere Soler-PalacínDepartment of Pediatrics, Obstetrics and Gynecology, Preventive Medicine and Public Health, Universitat Autònoma de Barcelona, Barcelona, Catalonia, Spain.
Jacques G RivièreDepartment of Pediatrics, Obstetrics and Gynecology, Preventive Medicine and Public Health, Universitat Autònoma de Barcelona, Barcelona, Catalonia, Spain.
Siobhán O BurnsInstitute for Immunity and Transplantation, University College London, London, United Kingdom.
Nicholas L RiderVirginia Tech Carilion School of Medicine, Department of Health Systems and Implementation Science, Roanoke, VA, United States.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Primary immune deficiencies (PI) are rare diseases associated with frequent, severe infections, inflammatory and autoimmune diseases and/or cancer. Because of the variability in presentation, undiagnosed PI patients can be encountered by many different medical specialists. A lack of awareness of and the rarity of PI can lead to delayed diagnosis particularly among primary care physicians and non-immunology specialists. These delays can lead to irreversible sequelae, decreased quality of life and premature mortality. In this review, we describe two projects designed to decrease the time to diagnosis in PI patients: 1) the expert-driven PIDCAP project conducted in Spain to promote early diagnosis in the primary care setting, and 2) a multi-modal data-driven approach using artificial intelligence and machine learning to identify individuals at high risk for PI. Both approaches aim to create widely available tools to promote early diagnosis and treatment of PI. Initial results have been positive. Future directions include larger studies and potentially combining expert-driven and data-driven approaches.

Indexed as

Artificial IntelligencePrimary Immunodeficiency DiseasesEarly DiagnosisHumansMachine Learningartificial intelligencediagnostic delayinborn errors of immunitymachine learningprimary immunodeficiencyscreening

Identifiers

PMID40709193
PMCPMC12286987

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.