REVIEW article
Front. Immunol.
Sec. Primary Immunodeficiencies
Volume 16 - 2025 | doi: 10.3389/fimmu.2025.1593897
New tools for diagnosis of primary immunodeficiencies: from awareness to artificial intelligence
Provisionally accepted
- 1Vall d'Hebron University Hospital, Barcelona, Spain
- 2Vall d'Hebron Research Institute (VHIR), Barcelona, Catalonia, Spain
- 3Hospital Infantil I de la Dona Vall d'Hebron, Vall d'Hebron Barcelona Hospital Campus, Barcelona, Spain
- 4Jeffrey Modell Diagnostic and Research Center for Primary Immunodeficiencies, Barcelona, Balearic Islands, Spain
- 5Institute of Immunity and Transplantation, University College London, London, England, United Kingdom
- 6Department of Immunology, Royal Free Hospital, London, England, United Kingdom
- 7Virginia Tech Carilion School of Medicine, Department of Health Systems and Implementation Science, Roanoke, Virginia, United States
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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 expertdriven and data-driven approaches.
Keywords: inborn errors of immunity, diagnostic delay, screening, artificial intelligence, machine learning, Primary immunodeficiency
Received: 14 Mar 2025; Accepted: 23 Jun 2025.
Copyright: © 2025 Soler-Palacín, Rivière, Burns and Rider. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Pere Soler-Palacín, Vall d'Hebron University Hospital, Barcelona, Spain
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