Journals / Acta Medica Nicomedia / 2026 / Cilt: 9

USE OF ARTIFICIAL INTELLIGENCE IN THE DIAGNOSIS AND TREATMENT OF ALLERGIC DISEASES

YAPAY ZEKANIN ALERJİK HASTALIKLARIN TANI VE TEDAVİSİNDE KULLANIMI

Pages
1–8
Publication Date
2026-06-19
🏛️Bibliographic metadata and full text access for this article are provided via DergiPark.
DergiPark Record ↗
✨

AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

In this review, it is aimed to explain the methods of using Artificial Intelligence in the diagnosis and treatment of allergic diseases in the light of literature data.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:Artificial Intelligence is the use of advanced computational algorithms to model complex human cognitive abilities by learning and adapting to collected data.
📊
Key Findings & Data:The studies and applications of Artificial Intelligence in the field of health have increased rapidly in the last decade.
💡
Conclusion & Impact:In this review, it is aimed to explain the methods of using Artificial Intelligence in the diagnosis and treatment of allergic diseases in the light of literature data.
🔬Design:Review & Systematic Survey
📍Scope:Comprehensive Sample
⚙️Variables:Yapay Zeka · alerji · teşhis

Abstract

Artificial Intelligence is the use of advanced computational algorithms to model complex human cognitive abilities by learning and adapting to collected data. The studies and applications of Artificial Intelligence in the field of health have increased rapidly in the last decade. In parallel with these developments in the field of health, the use of Artificial Intelligence and machine-based learning processes in the field of allergy is also increasing. The aim of using Artificial Intelligence in pediatric allergy topics such as asthma, allergic rhinitis, food allergy is generally to re-evaluate previously diagnosed patients and to increase diagnostic competence by creating various algorithms accordingly. However, it has also been stated in studies that the use of Artificial Intelligence and related tools should increase in treatment follow-up and prognosis evaluation. In this review, it is aimed to explain the methods of using Artificial Intelligence in the diagnosis and treatment of allergic diseases in the light of literature data.

Özet

Yapay Zeka, toplanan verileri öğrenerek ve bunlara uyum sağlayarak karmaşık insan bilişsel yeteneklerini modellemek için gelişmiş hesaplama algoritmalarının kullanılmasıdır. Yapay Zeka’ nın sağlık alanındaki çalışmaları ve uygulamaları son on yılda hızla artmıştır. Sağlık alanındaki bu gelişmelere paralel olarak alerji alanında da Yapay Zeka ve makine temelli öğrenim süreçlerinin de kullanımı artış göstermektedir. Yapay zekanın astım, alerjik rinit, gıda alerjisi gibi pediatrik alerjinin konu başlıklarında kullanım amaçları genellikle daha önce tanı konulan hastaların tekrardan değerlendirilmesi ve buna göre çeşitli algoritmalar oluşturup tanısal yeterliliği arttırmaktır. Bununla birlikte tedavi takibi ve prognoz değerlendirmede de Yapay Zeka ve ilişkili araçların kullanımlarının artması gerektiği de çalışmalarda belirtilmiştir. Bu derlemede, Yapay Zeka’ nın alerjik hastalıkların tanı ve tedavilerinde kullanım metotlarının literatür verileri ışığında anlatılması amaçlanmıştır.

🔖

Cite This Article

Kılınçkaya, L., Özdemir, Ö. (2026). USE OF ARTIFICIAL INTELLIGENCE IN THE DIAGNOSIS AND TREATMENT OF ALLERGIC DISEASES. Acta Medica Nicomedia, Cilt: 9, 1–8. https://doi.org/10.53446/actamednicomedia.1596641

📄 Full Text PDF Viewer

📄

PDF görüntüleyici hazırlanıyor...

References (62)

  1. Rajpurkar P, Chen E, Banerjee O, Topol EJ. AI in health and medicine. Nat Med. 2022;28(1):31-38. doi:10.1038/s41591-021-01614-0
  2. Lopez-Jimenez F, Attia Z, Arruda-Olson AM, et al. Artificial Intelligence in Cardiology: Present and Future. Mayo Clin Proc. 2020;95(5):1015-1039. doi:10.1016/j.mayocp. 2020.01.038
  3. Rider NL, Srinivasan R, Khoury P. Artificial intelligence and the hunt for immunological disorders. Curr Opin Allergy Clin Immunol. 2020;20(6):565-573. doi:10.1097/ACI. 0000000000000691
  4. Ferrante G, Licari A, Fasola S, Marseglia GL, La Grutta S. Artificial intelligence in the diagnosis of pediatric allergic diseases. Pediatr Allergy Immunol. 2021;32(3):405-413. doi:10.1111/pai.13419
  5. Rudrapatna VA, Butte AJ. Opportunities and challenges in using real-world data for health care. J Clin Invest. 2020;130(2):565-574. doi:10.1172/JCI129197
  6. Khoury P, Srinivasan R, Kakumanu S, et al. A Framework for Augmented Intelligence in Allergy and Immunology Practice and Research-A Work Group Report of the AAAAI Health Informatics, Technology, and Education Committee. J Allergy Clin Immunol Pract. 2022;10(5):1178-1188. doi:10.1016/j.jaip.2022.01.047
  7. Sastre J. Molecular diagnosis in allergy. Clin Exp Allergy. 2010;40(10):1442-1460. doi:10.1111/j.13652222. 2010.03585.x
  8. Stringari G, Tripodi S, Caffarelli C, et al. The effect of component-resolved diagnosis on specific immunotherapy prescription in children with hay fever. J Allergy Clin Immunol. 2014;134(1):75-81. doi:10.1016/j.jaci. 2014.01.042
  9. Melioli G, Spenser C, Reggiardo G, et al. Allergenius, an expert system for the interpretation of allergen microarray results. World Allergy Organ J. 2014;7(1):15. doi:10.1186/1939-4551-7-15
  10. Sagheb E, Wi CI, Yoon J, et al. Artificial Intelligence Assesses Clinicians' Adherence to Asthma Guidelines Using Electronic Health Records. J Allergy Clin Immunol Pract. 2022;10(4):1047-1056.e1. doi:10.1016/j.jaip.2021.11.004
  11. Joumaa H, Sigogne R, Maravic M, Perray L, Bourdin A, Roche N. Artificial intelligence to differentiate asthma from COPD in medico-administrative databases. BMC Pulm Med. 2022;22(1):357. doi:10.1186/s12890-022-02144-2
  12. Kaplan A, Cao H, FitzGerald JM, et al. Artificial Intelligence/Machine Learning in Respiratory Medicine and Potential Role in Asthma and COPD Diagnosis. J Allergy Clin Immunol Pract. 2021;9(6):2255-2261. doi:10.1016/ j.jaip.2021.02.014
  13. Hurst JH, Zhao C, Hostetler HP, Ghiasi Gorveh M, Lang JE, Goldstein BA. Environmental and clinical data utility in pediatric asthma exacerbation risk prediction models. BMC Med Inform Decis Mak. 2022;22(1):108. doi:10.1186/s12911-022-01847-0
  14. Jiao T, Schnitzer ME, Forget A, Blais L. Identifying asthma patients at high risk of exacerbation in a routine visit: A machine learning model. Respir Med. 2022;198:106866. doi:10.1016/j.rmed.2022.10686615.
  15. Inselman JW, Jeffery MM, Maddux JT, Lam RW, Shah ND, Rank MA, Ngufor CG. A prediction model for asthma exacerbations after stopping asthma biologics. Ann Allergy Asthma Immunol. 2023;130(3):305-311. doi:10.1016/j.anai.2022.11.025.
  16. D'Amato M, Ambrosino P, Simioli F, et al. A machine learning approach to characterize patients with asthma exacerbation attending an acute care setting. Eur J Intern Med. 2022;104:66-72. doi:10.1016/j.ejim.2022.07.019
  17. Lugogo NL, DePietro M, Reich M, et al. A Predictive Machine Learning Tool for Asthma Exacerbations: Results from a 12-Week, Open-Label Study Using an Electronic Multi-Dose Dry Powder Inhaler with Integrated Sensors. J Asthma Allergy. 2022;15:1623-1637. doi:10.2147/JAA.S377631
  18. Tsang KCH, Pinnock H, Wilson AM, Salvi D, Shah SA. Predicting asthma attacks using connected mobile devices and machine learning: the AAMOS-00 observational study protocol. BMJ Open. 2022;12(10):e064166. doi:10.1136/bmjopen-2022-064166
  19. Ramirez AH, Sulieman L, Schlueter DJ, et al. The All of Us Research Program: Data quality, utility, and diversity. Patterns (N Y). 2022;3(8):100570. doi:10.1016/j.patter.2022.100570
  20. Kurnat-Thoma E, Baranova A, Baird P, et al. Recent Advances in Systems and Network Medicine: Meeting Report from the First International Conference in Systems and Network Medicine. Syst Med (New Rochelle). 2020;3(1):22-35. doi:10.1089/sysm.2020.0001
  21. Z Zhang XA, Yates A, Vasilevsky N, et al. Semantic integration of clinical laboratory tests from electronic health records for deep phenotyping and biomarker discovery. NPJ Digit Med. 2019;2:32. doi:10.1038/s41746-019-0110-4
  22. Ray A, Das J, Wenzel SE. Determining asthma endotypes and outcomes: Complementing existing clinical practice with modern machine learning. Cell Rep Med. 2022;3(12):100857. doi:10.1016/j.xcrm.2022.100857
  23. De A, Sarda A, Gupta S, Das S. Use of Artificial Intelligence in Dermatology. Indian J Dermatol. 2020;65(5):352-357. doi:10.4103/ijd.IJD_418_20
  24. Eyerich K, Brown SJ, Perez White BE, et al. Human and computational models of atopic dermatitis: A review and perspectives by an expert panel of the International Eczema Council. J Allergy Clin Immunol. 2019;143(1):36-45. doi:10.1016/j.jaci.2018.10.033
  25. Prosperi MC, Belgrave D, Buchan I, Simpson A, Custovic A. Challenges in interpreting allergen microarrays in relation to clinical symptoms: a machine learning approach. Pediatr Allergy Immunol. 2014;25(1):71-79. doi:10.1111/pai.12139
  26. Fu D, Chuanliang Z, Jingdong Y, et al. Artificial intelligence applications in allergic rhinitis diagnosis: Focus on ensemble learning. Asia Pac Allergy. 2024;14(2):56-62. doi:10.5415/apallergy.0000000000000126
  27. Duverdier A, Custovic A, Tanaka RJ. Data-driven research on eczema: Systematic characterization of the field and recommendations for the future. Clin Transl Allergy. 2022;12(6):e12170. doi:10.1002/clt2.12170
  28. Ghosh D, Ding L, Sivaprasad U, et al. Multiple Transcriptome Data Analysis Reveals Biologically Relevant Atopic Dermatitis Signature Genes and Pathways. PLoS One. 2015;10(12):e0144316. doi:10.1371/journal.pone.0144316
  29. Yamamoto-Hanada K, Kawakami E, Saito-Abe M, et al. Exploratory analysis of plasma cytokine/chemokine levels in 6-year-old children from a birth cohort study. Cytokine. 2020 Jun;130:155051. doi: 10.1016/j.cyto.2020.155051.
  30. Aggarwal SLP. Data augmentation in dermatology image recognition using machine learning. Skin Res Technol. 2019;25(6):815-820. doi:10.1111/srt.12726
  31. Liu Y, Jain A, Eng C, et al. A deep learning system for differential diagnosis of skin diseases. Nat Med. 2020;26(6):900-908. doi:10.1038/s41591-020-0842-3
  32. Guimarães P, Batista A, Zieger M, Kaatz M, Koenig K. Artificial Intelligence in Multiphoton Tomography: Atopic Dermatitis Diagnosis. Sci Rep. 2020;10(1):7968. Published 2020 May 14. doi:10.1038/s41598-020-64937-x
  33. Park S, Saw SN, Li X, et al. Model learning analysis of 3D optoacoustic mesoscopy images for the classification of atopic dermatitis. Biomed Opt Express. 2021;12(6):3671-3683. doi:10.1364/BOE.415105
  34. Aguirre J, Schwarz M, Garzorz N, Omar M, Buehler A, Eyerich K, et al. Precision assessment of label-free psoriasis biomarkers with ultra-broadband optoacoustic mesoscopy. Nat Biomed Eng. 2017;1(5):0068. doi:10.1038/s41551-017-0068
  35. Schmitt J, Langan S, Deckert S, et al. Assessment of clinical signs of atopic dermatitis: a systematic review and recommendation. J Allergy Clin Immunol. 2013;132(6):1337-1347. doi:10.1016/j.jaci.2013.07.008
  36. Medela A, Mac Carthy T, Aguilar Robles SA, Chiesa-Estomba CM, Grimalt R. Automatic SCOring of Atopic Dermatitis Using Deep Learning: A Pilot Study. JID Innov. 2022;2(3):100107. doi:10.1016/j.xjidi.2022.100107
  37. Hurault G, Domínguez-Hüttinger E, Langan SM, Williams HC, Tanaka RJ. Personalized prediction of daily eczema severity scores using a mechanistic machine learning model. Clin Exp Allergy. 2020;50(11):1258-1266. doi:10.1111/cea.13717
  38. Hurault G, Stalder JF, Mery S, et al. EczemaPred: A computational framework for personalised prediction of eczema severity dynamics. Clin Transl Allergy. 2022;12(3):e12140. doi:10.1002/clt2.12140
  39. Holm JG, Hurault G, Agner T, et al. Immunoinflammatory Biomarkers in Serum Are Associated with Disease Severity in Atopic Dermatitis. Dermatology. 2021;237(4):513-520. doi:10.1159/000514503
  40. Maintz L, Welchowski T, Herrmann N, et al. Machine Learning-Based Deep Phenotyping of Atopic Dermatitis: Severity-Associated Factors in Adolescent and Adult Patients. JAMA Dermatol. 2021;157(12):1414-1424. doi:10.1001/jamadermatol.2021.3668
  41. Clayton K, Vallejo A, Sirvent S, et al. Machine learning applied to atopic dermatitis transcriptome reveals distinct therapy-dependent modification of the keratinocyte immunophenotype. Br J Dermatol. 2021;184(5):913-922. doi:10.1111/bjd.19431
  42. Acharjee A, Gribaleva E, Bano S, Gkoutos GV. Multi-omics-based identification of atopic dermatitis target genes and their potential associations with metabolites and miRNAs. Am J Transl Res. 2021;13(12):13697-13709.
  43. Jiang Z, Li J, Kong N, et al. Accurate diagnosis of atopic dermatitis by combining transcriptome and microbiota data with supervised machine learning. Sci Rep. 2022;12(1):290. doi:10.1038/s41598-021-04373-7
  44. Zeldin J, Chaudhary PP, Spathies J, et al. Exposure to isocyanates predicts atopic dermatitis prevalence and disrupts therapeutic pathways in commensal bacteria. Sci Adv. 2023;9(1):eade8898. doi:10.1126/sciadv.ade8898
  45. Fadadu RP, Grimes B, Jewell NP, et al. Association of Wildfire Air Pollution and Health Care Use for Atopic Dermatitis and Itch. JAMA Dermatol. 2021;157(6):658-666. doi:10.1001/jamadermatol.2021.0179
  46. Gupta RS, Warren CM, Smith BM, et al. Prevalence and Severity of Food Allergies Among US Adults. JAMA Netw Open. 2019;2(1):e185630. doi:10.1001/jamanetworkopen. 2018.5630
  47. Gendel SM. Comparison of international food allergen labeling regulations. Regul Toxicol Pharmacol. 2012;63(2):279-285. doi:10.1016/j.yrtph.2012.04.007
  48. Croote D, Quake SR. Food allergen detection by mass spectrometry: the role of systems biology. NPJ Syst Biol Appl. 2016;2:16022. doi:10.1038/npjsba.2016.22
  49. Gupta R, Holdford D, Bilaver L, Dyer A, Holl JL, Meltzer D. The economic impact of childhood food allergy in the United States [published correction appears in JAMA Pediatr. 2013;167(11):1083]. JAMA Pediatr. 2013;167(11):1026-1031. doi:10.1001/jamapediatrics. 2013.2376
  50. Matsuo H, Yokooji T, Taogoshi T. Common food allergens and their IgE-binding epitopes. Allergol Int. 2015;64(4):332-343. doi:10.1016/j.alit.2015.06.009
  51. Sánchez J, Sánchez A. Epidemiology of food allergy in Latin America. Allergol Immunopathol (Madr). 2015;43(2):185-195. doi:10.1016/j.aller.2013.07.001
  52. Adedeji AA, Priyesh PV, Odugbemi AA. The Magnitude and Impact of Food Allergens and the Potential of AI-Based Non-Destructive Testing Methods in Their Detection and Quantification. Foods. 2024;13(7):994. doi:10.3390/ foods13070994
  53. Metwally AA, Yu PS, Reiman D, Dai Y, Finn PW, Perkins DL. Utilizing longitudinal microbiome taxonomic profiles to predict food allergy via Long Short-Term Memory networks. PLoS Comput Biol. 2019;15(2):e1006693. doi:10.1371/journal.pcbi.1006693
  54. Suprun M, Sicherer SH, Wood RA, et al. Early epitope-specific IgE antibodies are predictive of childhood peanut allergy. J Allergy Clin Immunol. 2020;146(5):1080-1088. doi:10.1016/j.jaci.2020.08.005
  55. Grinek S, Suprun M, Raghunathan R, et al. Epitope-Specific IgE at 1 Year of Age Can Predict Peanut Allergy Status at 5 Years. Int Arch Allergy Immunol. 2023;184(3):273-278. doi:10.1159/000526364
  56. Kuniyoshi Y, Tokutake H, Takahashi N, Kamura A, Yasuda S, Tashiro M. Machine learning approach and oral food challenge with heated egg. Pediatr Allergy Immunol. 2021;32(4):776-778. doi:10.1111/pai.13433
  57. Machnes-Maayan D, Yahia SH, Frizinsky S, et al. A clinical pathway for the diagnosis of sesame allergy in children. World Allergy Organ J. 2022;15(11):100713. doi:10.1016/j.waojou.2022.100713
  58. Suárez-Fariñas M, Suprun M, Chang HL, et al. Predicting development of sustained unresponsiveness to milk oral immunotherapy using epitope-specific antibody binding profiles. J Allergy Clin Immunol. 2019;143(3):1038-1046. doi:10.1016/j.jaci.2018.10.028
  59. MacMath D, Chen M, Khoury P. Artificial Intelligence: Exploring the Future of Innovation in Allergy Immunology. Curr Allergy Asthma Rep. 2023;23(6):351-362. doi:10.1007/s11882-023-01084-z
  60. He J, Baxter SL, Xu J, Xu J, Zhou X, Zhang K. The practical implementation of artificial intelligence technologies in medicine. Nat Med. 2019;25(1):30-36. doi:10.1038/s41591-018-0307-0
  61. Angus DC. Randomized Clinical Trials of Artificial Intelligence. JAMA. 2020;323(11):1043-1045. doi:10.1001/jama.2020.1039
  62. Matheny ME, Whicher D, Thadaney Israni S. Artificial Intelligence in Health Care: A Report From the National Academy of Medicine. JAMA. 2020;323(6):509-510. doi:10.1001/jama.2019.21579