Documents from the SIAIP Commissions

Issue 1 - 2025

Artificial Intelligence-Driven Innovations in Allergy

Authors

Key words: Artificial Intelligence, Allergology, Machine Learning, Personalized Medicine, Environmental Monitoring, Diagnostic Innovation
Publication Date: 2025-03-27

Abstract

Artificial Intelligence (AI) is transforming allergology by enhancing diagnostics, personalizing treatments, and optimizing patient management. From environmental forecasting to advanced diagnostics, AI leverages machine learning algorithms to analyze complex data, identify biomarkers, and predict allergic reactions. Despite its potential, challenges regarding data privacy, algorithmic bias, and integration into clinical workflows still need to be addressed. Interdisciplinary collaboration and ethical frameworks are essential to harnessing AI’s benefits and redefining the future of care of allergic diseases.

References

  1. Brighetti MA, Costa C, Menesatti P, et al. Multivariate statistical forecasting modeling to predict Poaceae pollen critical concentrations by meteoclimatic data. Aerobiologia 2014;30:25-33; https://doi.org/ 10.1007/s10453-013-9305-3.
  2. Suanno C, Aloisi I, Fernández-González D, et al. Pollen forecasting and its relevance in pollen allergen avoidance. Environ Res 2021;200:111150.https://doi.org/ 10.1016/j.envres.2021.111150.
  3. Zhang Y, Steiner AL. Projected climate-driven changes in pollen emission season length and magnitude over the continental United States. Nat Commun 2022;13:1234; https://doi.org/ 10.1038/s41467-022-28764-0.
  4. Sharma S, Kumar R, Gupta M, et al. PolRam: a Machine Learning Based Web-Solution to Forecast and Alleviate Pollen Outbreaks. In: 2024 3rd International Conference on Computational Modelling, Simulation and Optimization (ICCMSO) IEEE: Phuket, Thailand, 2024, pp. 66–70; https://doi.org/ 10.1109/ICCMSO61761.2024.00028.
  5. Matricardi PM, Potapova E, Forchert L, et al. Digital allergology: Towards a clinical decision support system for allergen immunotherapy. Pediatr Allergy Immunol 2020;31(Suppl 24):61-64. https://doi.org/10.1111/pai.13165.
  6. Tripodi S, Giannone A, Sfika I, et al. Digital technologies for an improved management of respiratory allergic diseases: 10 years of clinical studies using an online platform for patients and physicians. Ital J Pediatr 2020;46:105; https://doi.org/ 10.1186/s13052-020-00870-z.
  7. Tsang KCH. Enhancing Asthma Self-Management with Environmental Passive-Monitoring Data and Machine Learning-Based Predictions. In: Mantas J, Hasman A, Demiris G, et al. eds. Studies in Health Technology and Informatics. IOS Press 2024. https://doi.org/ 10.3233/SHTI240510.
  8. Sato S, Ebisawa M. Precision allergy molecular diagnosis applications in food allergy. Curr Opin Allergy Clin Immunol 2024;24:129-137. https://doi.org/ 10.1097/ACI.0000000000000977.
  9. Hamilton RG, Kleine-Tebbe J. Molecular Allergy Diagnostics: Analytical Features That Support Clinical Decisions. Curr Allergy Asthma Rep 2015;15:57. https://doi.org/ 10.1007/s11882-015-0556-7.
  10. Bousquet J, Anto JM, Wickman M, et al. Are allergic multimorbidities and IgE polysensitization associated with the persistence or re-occurrence of foetal type 2 signalling? The M e DALL hypothesis. Allergy 2015;70:1062-1078; https://doi.org/ 10.1111/all.12637.
  11. Bashford T. AI Classification of Respiratory Illness Through Vocal Biomarkers and a Bespoke Articulatory Speech Protocol. Int J Simul Syst Sci Technol 2024;25(1). https://doi.org/ 10.5013/IJSSST.a.25.01.13.
  12. Neumann Ł, Nowak R, Stępień J, et al. Thermography based skin allergic reaction recognition by convolutional neural networks. Sci Rep 2022;12:2648; https://doi.org/ 10.1038/s41598-022-06460-9.
  13. Van Breugel M, Fehrmann RSN, Bügel M, et al. Current state and prospects of artificial intelligence in allergy. Allergy 2023;78:2623-2643. https://doi.org/ 10.1111/all.15849.
  14. Miller C, Manious M, Portnoy J. Artificial intelligence and machine learning for anaphylaxis algorithms. Curr Opin Allergy Clin Immunol 2024;24:305-312. https://doi.org/ 10.1097/ACI.0000000000001015.
  15. Arasi S, Castelli S, Di Fraia M, et al. @IT2020: An innovative algorithm for allergen immunotherapy prescription in seasonal allergic rhinitis. Clin Exp Allergy J Br Soc Allergy Clin Immunol 2021;51:821-828. https://doi.org/ 10.1111/cea.13867.
  16. Nagy M, Sisk B, Lai A, et al. Will artificial intelligence widen the therapeutic gap between children and adults? Pediatr Investig 2023;8:1. https://doi.org/ 10.1002/ped4.12407.
  17. Morcos G, Yi PH, Jeudy J. Applying Artificial Intelligence to Pediatric Chest Imaging: Reliability of Leveraging Adult-Based Artificial Intelligence Models. J Am Coll Radiol JACR 2023;20:742-747. https://doi.org/ 10.1016/j.jacr.2023.07.004.
  18. Bennett TD, Callahan TJ, Feinstein JA, et al. Data Science for Child Health. J Pediatr 2019;208:12-22. https://doi.org/ 10.1016/j.jpeds.2018.12.041.

Downloads

Authors

Edited by SIAIP New Digital Technologies Commission

Alessandra Gori - Department of Mother-Child, Urological Science, Sapienza University, Rome, Italy

Anna Maria Zicari - NESMOS Department, Faculty of Medicine and Psychology, Pediatric Unit Sant’Andrea Hospital, Sapienza University, Rome, Italy

Mario Barreto - NESMOS Department, Faculty of Medicine and Psychology, Pediatric Unit Sant’Andrea Hospital, Sapienza University, Rome, Italy

Auro Della Giustina - Private Practice, Parma, Italy

Ifigenia Sfika - Private Practice, Rome, Italy

Stefano Pattini - Pediatric Unit, Department of Medical and Surgical Sciences of the Mother, Children and Adults, University of Modena and Reggio Emilia, Modena, Italy

Alessandro Travaglini - Department of Biology, Tor Vergata University, Rome, Italy

Maria Antonietta Brighetti - Department of Biology, Tor Vergata University, Rome, Italy

Denise De Franco - Department of Biology, Tor Vergata University, Rome, Italy

Alessandro Di Menno Di Bucchianico - Italian Institute for Environmental Protection and Research (ISPRA), Rome, Italy

Salvatore Tripodi - Allergology Service, Policlinico Casilino, Rome, Italy

How to Cite
New Digital Technologies Commission, E. by S., Gori, A., Zicari, A. M., Barreto, M., Della Giustina, A., Sfika, I., Pattini, S., Travaglini, A., Brighetti, M. A., De Franco, D., Di Menno Di Bucchianico, A., & Tripodi, S. . (2025). Artificial Intelligence-Driven Innovations in Allergy. Italian Journal of Pediatric Allergy and Immunology, 39(1). https://doi.org/10.53151/2531-3916/2025-863
  • Abstract viewed - 41 times
  • pdf downloaded - 6 times