Artificial intelligence in clinical allergy practice: current status, challenges, and future directions

Abstract

Artificial intelligence (AI) is poised to transform clinical allergy practice by enhancing diagnostic accuracy, personalising treatment, and streamlining healthcare delivery. This narrative review critically examines the current landscape of AI in allergy care, spanning clinical workflows, diagnostics, immunotherapy, and research applications. AI-powered tools such as clinical decision support systems (CDSS), natural language processing (NLP), and conversational agents are being integrated into allergy services, offering improvements in documentation, risk stratification, and remote patient engagement-particularly in paediatric and multilingual settings. Diagnostic innovations include machine learning models that predict oral food challenge outcomes and interpret multi-omics data for personalised allergy phenotyping. AI also supports adaptive immunotherapy dosing, remote monitoring via wearable biosensors, and digital coaching to promote adherence. Federated learning and explainable AI (XAI) emerge as pivotal developments-enabling privacy-preserving collaboration and fostering trust among clinicians and patients. Despite these advancements, significant challenges remain. These include data inequities, algorithmic bias, lack of real-world validation, and regulatory ambiguity. The “black box” nature of many models risks undermining clinician confidence, while over-reliance on alerts could contribute to alarm fatigue. Ethical concerns-particularly around transparency, consent, and liability-require urgent attention. Equitable implementation demands robust governance, diverse training data, and inclusive design that prioritises patient safety. Looking ahead, AI has the potential to power digital twins, support augmented reality training, and enhance allergy surveillance through the integration of environmental and population-level data. With multidisciplinary collaboration, transparent oversight, and patient-centred innovation, AI can help build a more predictive, efficient, and equitable future for allergy care.

Keywords

artificial intelligence / clinical allergy / machine learning / immunotherapy / natural language processing / digital health

References

[1] Pawankar R, Canonica GW, Holgate ST, Lockey RF, Blaiss MS. WAO White Book on Allergy: Update 2013 [Internet]. Milwaukee (WI): World Allergy Organization; c2013 [cited 2025 May 27]. Available from: https://allergiy.net/uploads/ck/ExecSummary-2013-v6-hires.pdf

[2] Asher MI, Montefort S, Björkstén B, Lai CK, Strachan DP, Weiland SK, et al. ; ISAAC Phase Three Study Group. Worldwide time trends in the prevalence of symptoms of asthma, allergic rhinoconjunctivitis, and eczema in childhood: ISAAC Phases One and Three repeat multicountry cross-sectional surveys. Lancet. 2006; 368: 733-43.

[3] Gupta R, Sheikh A, Strachan DP, Anderson HR. Burden of allergic disease in the UK: secondary analyses of national databases. Clin Exp Allergy. 2004; 34: 520-6.

[4] Foong RX, du Toit G, Fox AT. Asthma, Food Allergy, and How They Relate to Each Other. Front Pediatr. 2017; 5: 89.

[5] Meeting the challenges of the national allergy crisis [Internet]. [cited 2025 May 27]. Available from: https://www.allergyuk.org/wp-content/uploads/2021/10/Meeting-the-challenges-of-the-national-allergy-crisis-2021.pdf

[6] Turner PJ, Baseggio Conrado A, Kallis C, O’Rourke E, Haider S, Ullah A, et al. Time trends in the epidemiology of food allergy in England: an observational analysis of Clinical Practice Research Datalink data. Lancet Public Health. 2024; 9: e664-73.

[7] Xu Y, Liu X, Cao X, Huang C, Liu E, Qian S, et al. Artificial intelligence: A powerful paradigm for scientific research. Innovation (Camb). 2021; 2: 100179.

[8] Yao J, Lim J, Lim GYS, Ong JCL, Ke Y, Tan TF, et al. Novel artificial intelligence algorithms for diabetic retinopathy and diabetic macular edema. Eye Vis (Lond). 2024; 11: 23.

[9] Gryak J, Georgievska A, Zhang J, Najarian K, Ravikumar R, Sanders G, et al. Prediction of pediatric peanut oral food challenge outcomes using machine learning. J Allergy Clin Immunol Glob. 2024; 3: 100252.

[10] Lee S, Kim HS. Prospect of Artificial Intelligence Based on Electronic Medical Record. J Lipid Atheroscler. 2021; 10: 282-90.

[11] Matricardi PM, Potapova E, Forchert L, Dramburg S, Tripodi S. Digital allergology: Towards a clinical decision support system for allergen immunotherapy. Pediatr Allergy Immunol. 2020; 31: 61-4.

[12] Vallamkonda S, Ortega CA, Lo YC, Blackley SV, Wang L, Seger DL, et al. Identifying and Reconciling Patients’ Allergy Information Within the Electronic Health Record. Stud Health Technol Inform. 2022; 290: 120-4.

[13] Xiao N, Huang X, Wu Y, Li B, Zang W, Shinwari K, et al. Opportunities and challenges with artificial intelligence in allergy and immunology: a bibliometric study. Front Med (Lausanne). 2025; 12: 1523902.

[14] Tang L, Li J, Fantus S. Medical artificial intelligence ethics: A systematic review of empirical studies. Digit Health. 2023; 9: 20552076231186064.

[15] De Vito G, Ferrucci F, Angelakis A. Design and evaluation of a CDSS for drug allergy management using LLMs and pharmaceutical data integration. arXiv [Preprint]. 2024 [cited 2025 May 27]. Available from: https://www.researchgate.net/publication/384364087_Design_and_Evaluation_of_a_CDSS_for_Drug_Allergy_Management_Using_LLMs_and_Pharmaceutical_Data_Integration

[16] Elhaddad M, Hamam S. AI-Driven Clinical Decision Support Systems: An Ongoing Pursuit of Potential. Cureus. 2024; 16: e57728.

[17] Matui P, Wyatt JC, Pinnock H, Sheikh A, McLean S. Computer decision support systems for asthma: a systematic review. NPJ Prim Care Respir Med. 2014; 24: 14005.

[18] Chen Z, Liang N, Zhang H, Li H, Yang Y, Zong X, et al. Harnessing the power of clinical decision support systems: challenges and opportunities. Open Heart. 2023; 10: e002432.

[19] Ledda AG, Can Bostan O, Del Giacco S. Telemedicine and remote monitoring in asthma care: innovations triggered by the pandemic. Explor Asthma Allergy. 2025; 3: 100974.

[20] Maher C, Singh B, Wylde A, Chastin S. Virtual health assistants: a grand challenge in health communications and behavior change. Front Digit Health. 2024; 6: 1418695.

[21] Scharp D, Hobensack M, Davoudi A, Topaz M. Natural Language Processing Applied to Clinical Documentation in Post-acute Care Settings: A Scoping Review. J Am Med Dir Assoc. 2024; 25: 69-83.

[22] Fudickar S, Bantel C, Spieker J, Töpfer H, Stegeman P, Schiphorst Preuper HR, et al. Natural Language Processing of Referral Letters for Machine Learning-Based Triaging of Patients With Low Back Pain to the Most Appropriate Intervention: Retrospective Study. J Med Internet Res. 2024; 26: e46857.

[23] Goss FR, Plasek JM, Lau JJ, Seger DL, Chang FY, Zhou L. An evaluation of a natural language processing tool for identifying and encoding allergy information in emergency department clinical notes. AMIA Annu Symp Proc. 2014; 2014: 580-8.

[24] Lo YC, Varghese S, Blackley S, Seger DL, Blumenthal KG, Goss FR, et al. Reconciling Allergy Information in the Electronic Health Record After a Drug Challenge Using Natural Language Processing. Front Allergy. 2022; 3: 904923.

[25] Duggan MJ, Gervase J, Schoenbaum A, Hanson W, Howell JT 3rd, Sheinberg M, et al. Clinician Experiences With Ambient Scribe Technology to Assist With Documentation Burden and Efficiency. JAMA Netw Open. 2025; 8: e2460637.

[26] Juhn Y, Liu H. Artificial intelligence approaches using natural language processing to advance EHR-based clinical research. J Allergy Clin Immunol. 2020; 145: 463-9.

[27] Dumitru M, Berghi ON, Taciuc IA, Vrinceanu D, Manole F, Costache A. Could Artificial Intelligence Prevent Intraoperative Anaphylaxis? Reference Review and Proof of Concept. Medicina (Kaunas). 2022; 58: 1530.

[28] González-Díaz SN, Morais-Almeida M, Ansotegui IJ, Macouzet-Sánchez C, Ordóñez-Azuara YG, Camarena-Galván J, et al. Artificial intelligence in allergy practice: Digital transformation and the future of clinical care. World Allergy Organ J. 2025; 18: 101078.

[29] Goktas P, Damadoglu E. Future of allergy and immunology: Is artificial intelligence the key in the digital era? Ann Allergy Asthma Immunol. 2025; 134: 396-407.e2.

[30] Wu CP, Sleiman J, Fakhry B, Chedraoui C, Attaway A, Bhattacharyya A, et al. Novel Machine Learning Identifies 5 Asthma Phenotypes Using Cluster Analysis of Real-World Data. J Allergy Clin Immunol Pract. 2024; 12: 2084-91.e4.

[31] Fazlali M, Nasira M, Moravej A. AI-Driven Biomarker Discovery and Personalized Allergy Treatment: Utilizing Machine Learning and NGS. Curr Allergy Asthma Rep. 2025; 25: 27.

[32] Ridolo E, Incorvaia C, Heffler E, Cavaliere C, Paoletti G, Canonica GW. The Present and Future of Allergen Immunotherapy in Personalized Medicine. J Pers Med. 2022; 12: 774.

[33] Li Y, Xiong Y, Fan W, Wang K, Yu Q, Si L, et al. Sequential model for predicting patient adherence in subcutaneous immunotherapy for allergic rhinitis. Front Pharmacol. 2024; 15: 1371504.

[34] Murdaca G, Negrini S, Pellecchio M, Greco M, Schiavi C, Giusti F, et al. Update upon the infection risk in patients receiving TNF alpha inhibitors. Expert Opin Drug Saf. 2019; 18: 219-29.

[35] Shajari S, Kuruvinashetti K, Komeili A, Sundararaj U. The Emergence of AI-Based Wearable Sensors for Digital Health Technology: A Review. Sensors (Basel). 2023; 23: 9498.

[36] Kalmotia V. Artificial Intelligence in Allergy Treatment: A Decision Support. SSRN [Preprint]. 2025 [cited 2025 May 27]. Available from: http://dx.doi.org/10.2139/ssrn.5254133

[37] Chen Z, Min MR, Guo H, Cheng C, Clancy T, Ning X. T-Cell Receptor Optimization with Reinforcement Learning and Mutation Polices for Precision Immunotherapy. In: Tang H, editor. Research in Computational Molecular Biology. RECOMB 2023. Springer, Cham; 2023. pp. 174-91.

[38] Rony MKK, Parvin MR, Ferdousi S. Advancing nursing practice with artificial intelligence: Enhancing preparedness for the future. Nurs Open. 2024; 11: 10.1002/nop2.2070.

[39] Assegie TA. Evaluation of local interpretable model-agnostic explanation and Shapley additive explanation for chronic heart disease detection. Proc Eng Technol Innov. 2023; 23: 48-59.

[40] Miotto R, Wang F, Wang S, Jiang X, Dudley JT. Deep learning for healthcare: review, opportunities and challenges. Brief Bioinform. 2018; 19: 1236-46.

[41] General Data Protection Regulation [Internet]. [cited 2025 Jun 8]. Available from: https://gdpr-info.eu

[42] General Data Protection Regulation (GDPR) - information [Internet]. [cited 2025 Jun 8]. Available from: https://digital.nhs.uk/data-and-information/keeping-data-safe-and-benefitting-the-public/gdpr

[43] Kumar Malesu V. WHO issues ethical guidelines for AI in healthcare, focusing on large multi-modal models [Internet]. AZoNetwork; c2000- 2025 [cited 2025 Jun 8]. Available from: https://www.news-medical.net/news/20240123/WHO-issues-ethical-guidelines-for-AI-in-healthcare-focusing-on-large-multi-modal-models.aspx

[44] Good Machine Learning Practice for Medical Device Development: Guiding Principles [Internet]. [cited 2025 Sep 4]. Available from: https://www.fda.gov/medical-devices/software-medical-device-samd/good-machine-learning-practice-medical-device-development-guiding-principles

[45] Alkhanbouli R, Matar Abdulla Almadhaani H, Alhosani F, Simsekler MCE. The role of explainable artificial intelligence in disease prediction: a systematic literature review and future research directions. BMC Med Inform Decis Mak. 2025; 25: 110.

[46] Pottle J. Virtual reality and the transformation of medical education. Future Healthc J. 2019; 6: 181-5.