| Title: |
Proceedings of the second Artificial Intelligence in Primary Immunodeficiency (AIPI) meeting |
| Authors: |
Rivière, Jacques G.; Bastarache, Lisa; Campos, Luiza C.; Carot-Sans, Gerard; Chin, Aaron; Chunara, Rumi; Cunningham-Rundles, Charlotte; Erra, Lorenzo; Farmer, Jocelyn; Garcelon, Nicolás; Hsieh, Elena; Leavis, Helen; Lee, Seungwon; Liu, Liangying; Kusters, Maaike A.; Lloyd, Brian C.; Martinson, Alexandra K.; Mester, Rachel; Moore, Justin B.; Moshous, Despina; Orange, Jordan S.; Parrish, Nefatia; Parker, Sarah Henrickson; Pasaniuc, Bogdan; Peng, Xiao P.; Pergent, Martine; Piera-Jiménez, Jordi; Quinn, Jessica; Ramesh, Sidharth; Roberts, Kirk; Robinson, Peter N.; Savova, Guergana; Scalchunes, Christopher; Seidel, Markus G.; Simoneau, Rachel; Soler-Palacín, Pere; Sullivan, Kathleen; Van Gijn, Marielle; Wi, Chung-Il; Zhou, Dawei; Tenembaum, Vanessa; Butte, Manish J.; Rider, Nicholas L. |
| Publication Year: |
2025 |
| Collection: |
Universitat Autònoma de Barcelona: Dipòsit Digital de Documents de la UAB |
| Subject Terms: |
Artificial intelligence; Inborn errors of immunity; Primary immunodeficiency; Electronic health records; Machine learning; Large language models; Health equity; omics; Clinical decision support; Implementation science; Patient-centered AI; AI scalability; AI rare diseases |
| Description: |
Altres ajuts: acords transformatius de la UAB ; The use of artificial intelligence (AI) in inborn errors of immunity offers transformative potential in diagnostics and disease management but faces multiple challenges that were discussed at the second Artificial Intelligence in Primary Immunodeficiency conference, held in New York City (March 19-22, 2025). The conference addressed 7 themes: predictive diagnostic algorithms, health equity, industry collaboration, advanced computational tools like large language models, patient-led AI initiatives, multiomics integration, and implementation science. Discussions highlighted the growing impact of AI on diagnostics, genomics, and health systems, emphasizing the need for high-quality, diverse datasets and ethical safeguards to ensure equitable application. Participants stressed that AI alone cannot resolve systemic inequities or delays in diagnosis. Challenges such as the lack of harmonized datasets, the complexity of integrating multiomics data, ethical concerns, and the difficulty of adapting solutions to low-resource settings were emphasized. Additionally, the use implementation science was pointed out as one of the major challenges to ensure applicability and scalability in real-world settings. This requires overcoming resistance to adoption, addressing infrastructure gaps, and ensuring regulatory compliance. Collaboration across academia, clinicians, patients, regulators, and industry is essential to ensure AI delivers equitable, lasting benefits for individuals with inborn errors of immunity. |
| Document Type: |
article in journal/newspaper |
| File Description: |
application/pdf |
| Language: |
English |
| ISSN: |
10976825 |
| Relation: |
The journal of allergy and clinical immunology; Vol. 157, Num. 2 (February 2025), p. 307-315; https://ddd.uab.cat/record/321797; urn:10.1016/j.jaci.2025.09.002; urn:oai:ddd.uab.cat:321797; urn:articleid:10976825v157n2p307; urn:scopus_id:105018874504 |
| Availability: |
https://ddd.uab.cat/record/321797 |
| Rights: |
open access ; Aquest document està subjecte a una llicència d'ús Creative Commons. Es permet la reproducció total o parcial, la distribució, la comunicació pública de l'obra i la creació d'obres derivades, sempre que no sigui amb finalitats comercials, i sempre que es reconegui l'autoria de l'obra original. ; https://creativecommons.org/licenses/by-nc/4.0/ |
| Accession Number: |
edsbas.837D0636 |
| Database: |
BASE |