Application of Artificial intelligence on the field of immunology

Authors

Keywords:

Alergia e Inmunología, Análisis de Datos, Inteligencia Artificial, Aprendizaje Profundo, Aprendizaje Automático.

Abstract

Immunology has evolved over the years, achieving rapid growth through the use of emerging technologies for its development. Artificial intelligence represents a useful tool for the development of medical specialties, including immunology. A search for information in the Scopus, PubMed/MedLine and SciELO databases on the applications of artificial intelligence in the field of immunology was carried out. To retrieve the information, a search strategy was employed by combining terms with Boolean operators. It was found that Artificial Intelligence has multiple applications in the field of immunology, being useful for the analysis of genomic and immunogenomic data, prediction of immune responses in different situations such as allergy and organ transplants, vaccine development, early detection, stratification and prediction of diseases, immunotherapy in cancer treatment and response to pandemics, among others. However, it is not without its challenges, such as data readability and inadequate infrastructures. Similarly, there are risks, such as inadequate interpretations of the analyses and ethical implications.

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Author Biography

Adrián Alejandro Vitón-Castillo, Universidad de Ciencias Médicas de Pinar del Río. Hospital Pediátrico Provincial Docente “Pepe Portilla”. Departamento de Inmunología. Pinar del Río, Cuba.

Editor Jefe revista Data and Metadata (Scopus-Indexed).

Universidad de Ciencias Médicas de Pinar del Río. Hospital Pediátrico Provincial Docente “Pepe Portilla”. Departamento de Inmunología. Pinar del Río, Cuba.

Editor de Producción, A&G Editors. 

 

References

1. Farzan R. Artificial intelligence in Immuno-genetics. Bioinformation [Internet]. 31 de enero de 2024 [citado 17 de noviembre de 2024];20(1):29. Disponible en: https://pmc.ncbi.nlm.nih.gov/articles/PMC10859949/

2. Germain RN, Allen EMV, Trynka G, Tsang JS, Grün D, Kiemen AL, et al. AI and immunology. Immunity [Internet]. 11 de junio de 2024 [citado 17 de noviembre de 2024];57(6):1177-81. Disponible en: https://www.cell.com/immunity/abstract/S1074-7613(24)00270-X

3. Moza Villalobos F, Natividad Villanueva J, Meneses Claudio B. Use of Convolutional Neural Networks (CNN) to recognize the quality of oranges in Peru by 2023. Data and Metadata [Internet]. 29 de diciembre de 2023 [citado 3 de noviembre de 2024];2:175. Disponible en: https://dm.ageditor.ar/index.php/dm/article/view/117

4. Boussouf Z, Amrani H, Zerhouni Khal M, Daidai F. Artificial Intelligence in Education: a Systematic Literature Review. Data and Metadata [Internet]. 5 de julio de 2024 [citado 3 de noviembre de 2024];3:288. Disponible en: https://dm.ageditor.ar/index.php/dm/article/view/310

5. Recalde Drouet EM, Tello Salazar DM, Charro Domínguez TL, Catota Pinthsa PJ. Analysis of the repercussions of Artificial Intelligence in the Personalization of the Virtual Educational Process in Higher Education Programs. Data and Metadata [Internet]. 1 de enero de 2024 [citado 3 de noviembre de 2024];3:386. Disponible en: https://dm.ageditor.ar/index.php/dm/article/view/277

6. Albarracín Vanoy RJ. Logistics 4.0: Exploring Artificial Intelligence Trends in Efficient Supply Chain Management. Data and Metadata [Internet]. 14 de diciembre de 2023 [citado 3 de noviembre de 2024];2:145. Disponible en: https://doi.org/10.56294/dm2023145

7. Mejías M, Guarate Coronado YC, Jiménez Peralta AL. Artificial intelligence in the field of nursing: attendance, administration and education implications. Salud, Ciencia y Tecnología [Internet]. 22 de octubre de 2022 [citado 3 de noviembre de 2024];2:88. Disponible en: https://sct.ageditor.ar/index.php/sct/article/view/163

8. Anwar D, Faizanuddin M, Fatima S, Dayal R. Transforming Supply Chain Finance with AI and IoT for Greater Inclusivity, Efficiency, and Intelligence. LatIA [Internet]. 1 de enero de 2025 [citado 17 de noviembre de 2024];3:121. Disponible en: https://latia.ageditor.uy/index.php/latia/article/view/121

9. Dei H. The use of AI in the organization of local government work. LatIA [Internet]. 1 de enero de 2025 [citado 17 de noviembre de 2024];3:123. Disponible en: https://doi.org/10.62486/latia2025123

10. Gama Espinosa JC, Leiva Sánchez LM, Fajardo Pereira MA. Benefits of Artificial Intelligence in human talent management. Multidisciplinar (Montevideo) [Internet]. 25 de julio de 2024 [citado 17 de noviembre de 2024];1:14. Disponible en: https://doi.org/10.62486/agmu202314

11. Valencia-Contrera M, Rivera-Rojas F, Villa-Velasquez J, Cancino-Jiménez D. Use of artificial intelligence in nursing. LatIA [Internet]. 2 de septiembre de 2024 [citado 3 de noviembre de 2024];2:92. Disponible en: https://latia.ageditor.uy/index.php/latia/article/view/92

12. Nabaouia L, Douzi S, Bouabid EO. Explainable machine learning for coronary artery disease risk assessment and prevention. Data and Metadata [Internet]. 29 de diciembre de 2023 [citado 3 de noviembre de 2024];2:65. Disponible en: https://dm.ageditor.ar/index.php/dm/article/view/159

13. Injante R, Julca M. Detection of diabetic retinopathy using artificial intelligence: an exploratory systematic review. LatIA [Internet]. 2 de septiembre de 2024 [citado 3 de noviembre de 2024];2:112. Disponible en: https://latia.ageditor.uy/index.php/latia/article/view/112

14. Oumoulylte M, Omari Alaoui A, Farhaoui Y, El Allaoui A, Bahri A. Convolutional Neural Network-Based Approach For Skin Lesion Classification. Data and Metadata [Internet]. 27 de diciembre de 2023 [citado 3 de noviembre de 2024];2:171. Disponible en: https://dm.ageditor.ar/index.php/dm/article/view/121

15. Muhyeeddin A, Mowafaq SA, Al-Batah MS, Mutaz AW. Advancing Medical Image Analysis: The Role of Adaptive Optimization Techniques in Enhancing COVID-19 Detection, Lung Infection, and Tumor Segmentation. LatIA [Internet]. 29 de septiembre de 2024 [citado 3 de noviembre de 2024];2:74. Disponible en: https://latia.ageditor.uy/index.php/latia/article/view/74

16. Agrawal A, Maan V. Enhanced Brain Tumor Segmentation and Size Estimation in MRI Samples using Hybrid Optimization. Data and Metadata [Internet]. 1 de enero de 2024 [citado 3 de noviembre de 2024];3:408. Disponible en: https://dm.ageditor.ar/index.php/dm/article/view/268

17. Sidiq M, Chahal A, Gupta S, Reddy Vajrala K. Advancement, utilization, and future outlook of Artificial Intelligence for physiotherapy clinical trials in India: An overview. Interdisciplinary Rehabilitation / Rehabilitacion Interdisciplinaria [Internet]. 24 de diciembre de 2023 [citado 17 de noviembre de 2024];4:73. Disponible en: https://doi.org/10.56294/ri202473

18. Wahed MA, Alqaraleh M, Salem Alzboon M, Subhi Al-Batah M. Evaluating AI and Machine Learning Models in Breast Cancer Detection: A Review of Convolutional Neural Networks (CNN) and Global Research Trends. LatIA [Internet]. 1 de enero de 2025 [citado 17 de noviembre de 2024];3:117. Disponible en: https://doi.org/10.62486/latia2025117

19. Schultze JL, Büttner M, Becker M. Swarm immunology: harnessing blockchain technology and artificial intelligence in human immunology. Nat Rev Immunol [Internet]. julio de 2022 [citado 17 de noviembre de 2024];22(7):401-3. Disponible en: https://www.nature.com/articles/s41577-022-00740-1

20. Khoury P, Srinivasan R, Kakumanu S, Ochoa S, Keswani A, Sparks R, 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. The Journal of Allergy and Clinical Immunology: In Practice [Internet]. mayo de 2022 [citado 3 de noviembre de 2024];10(5):1178-88. Disponible en: https://linkinghub.elsevier.com/retrieve/pii/S221321982200143X

21. Zhang GL, Lin HH, Keskin DB, Reinherz EL, Brusic V. Dana-Farber repository for machine learning in immunology. Journal of Immunological Methods [Internet]. noviembre de 2011 [citado 3 de noviembre de 2024];374(1-2):18-25. Disponible en: https://linkinghub.elsevier.com/retrieve/pii/S0022175911001773

22. Karahalil B. Overview of Systems Biology and Omics Technologies. CMC [Internet]. 5 de diciembre de 2016 [citado 3 de noviembre de 2024];23(37):4221-30. Disponible en: http://www.eurekaselect.com/openurl/content.php?genre=article&issn=0929-8673&volume=23&issue=37&spage=4221

23. Mersha TB, Afanador Y, Johansson E, Proper SP, Bernstein JA, Rothenberg ME, et al. Resolving Clinical Phenotypes into Endotypes in Allergy: Molecular and Omics Approaches. Clinic Rev Allerg Immunol [Internet]. abril de 2021 [citado 3 de noviembre de 2024];60(2):200-19. Disponible en: https://link.springer.com/10.1007/s12016-020-08787-5

24. Goktas P, Damadoglu E. Future of Allergy and Immunology: Is AI the Key in the Digital Era? Annals of Allergy, Asthma & Immunology [Internet]. octubre de 2024 [citado 3 de noviembre de 2024];S1081120624015953. Disponible en: https://linkinghub.elsevier.com/retrieve/pii/S1081120624015953

25. Naruka V, Arjomandi Rad A, Subbiah Ponniah H, Francis J, Vardanyan R, Tasoudis P, et al. Machine learning and artificial intelligence in cardiac transplantation: A systematic review. Artificial Organs [Internet]. septiembre de 2022 [citado 17 de noviembre de 2024];46(9):1741-53. Disponible en: https://onlinelibrary.wiley.com/doi/10.1111/aor.14334

26. Zhang GL, Ansari HR, Bradley P, Cawley GC, Hertz T, Hu X, et al. Machine learning competition in immunology – Prediction of HLA class I binding peptides. Journal of Immunological Methods [Internet]. noviembre de 2011 [citado 3 de noviembre de 2024];374(1-2):1-4. Disponible en: https://linkinghub.elsevier.com/retrieve/pii/S0022175911002559

27. Jabbari P, Rezaei N. Artificial intelligence and immunotherapy. Expert Review of Clinical Immunology [Internet]. 3 de julio de 2019 [citado 17 de noviembre de 2024];15(7):689-91. Disponible en: https://www.tandfonline.com/doi/full/10.1080/1744666X.2019.1623670

28. Deng J, Zhou X, Zhang P, Cheng W, Liu M, Tian J. IEPAPI: a method for immune epitope prediction by incorporating antigen presentation and immunogenicity. Briefings in Bioinformatics [Internet]. 20 de julio de 2023 [citado 17 de noviembre de 2024];24(4):bbad171. Disponible en: https://academic.oup.com/bib/article/doi/10.1093/bib/bbad171/7179756

29. Olawade DB, Teke J, Fapohunda O, Weerasinghe K, Usman SO, Ige AO, et al. Leveraging artificial intelligence in vaccine development: A narrative review. Journal of Microbiological Methods [Internet]. septiembre de 2024 [citado 16 de noviembre de 2024];224:106998. Disponible en: https://linkinghub.elsevier.com/retrieve/pii/S0167701224001106

30. Asediya VS, Anjaria PA, Mathakiya RA, Koringa PG, Nayak JB, Bisht D, et al. Vaccine development using artificial intelligence and machine learning: A review. International Journal of Biological Macromolecules [Internet]. diciembre de 2024 [citado 16 de noviembre de 2024];282:136643. Disponible en: https://linkinghub.elsevier.com/retrieve/pii/S0141813024074518

31. Pickett G, Motazedi T, Kutac C, Cahill G, Cunnigham-Rundles C, Fuleihan RL, et al. Infection Phenotypes Among Patients with Primary Antibody Deficiency Mined from a US Patient Registry. J Clin Immunol [Internet]. febrero de 2021 [citado 3 de noviembre de 2024];41(2):374-81. Disponible en: http://link.springer.com/10.1007/s10875-020-00916-1

32. Moingeon P. Artificial intelligence-driven drug development against autoimmune diseases. Trends in Pharmacological Sciences [Internet]. julio de 2023 [citado 16 de noviembre de 2024];44(7):411-24. Disponible en: https://linkinghub.elsevier.com/retrieve/pii/S0165614723000883

33. Rider NL, Srinivasan R, Khoury P. Artificial intelligence and the hunt for immunological disorders. Current Opinion in Allergy & Clinical Immunology [Internet]. diciembre de 2020 [citado 16 de noviembre de 2024];20(6):565-73. Disponible en: https://journals.lww.com/10.1097/ACI.0000000000000691

34. MacMath D, Chen M, Khoury P. Artificial Intelligence: Exploring the Future of Innovation in Allergy Immunology. Curr Allergy Asthma Rep [Internet]. 1 de junio de 2023 [citado 17 de noviembre de 2024];23(6):351-62. Disponible en: https://doi.org/10.1007/s11882-023-01084-z

35. Li T, Li Y, Zhu X, He Y, Wu Y, Ying T, et al. Artificial intelligence in cancer immunotherapy: Applications in neoantigen recognition, antibody design and immunotherapy response prediction. Seminars in Cancer Biology [Internet]. junio de 2023 [citado 16 de noviembre de 2024];91:50-69. Disponible en: https://linkinghub.elsevier.com/retrieve/pii/S1044579X23000305

36. Xu Z, Wang X, Zeng S, Ren X, Yan Y, Gong Z. Applying artificial intelligence for cancer immunotherapy. Acta Pharmaceutica Sinica B [Internet]. noviembre de 2021 [citado 16 de noviembre de 2024];11(11):3393-405. Disponible en: https://linkinghub.elsevier.com/retrieve/pii/S2211383521000459

37. Yang Y, Zhao Y, Liu X, Huang J. Artificial intelligence for prediction of response to cancer immunotherapy. Seminars in Cancer Biology [Internet]. diciembre de 2022 [citado 16 de noviembre de 2024];87:137-47. Disponible en: https://linkinghub.elsevier.com/retrieve/pii/S1044579X22002309

38. Li G, Iyer B, Prasath VBS, Ni Y, Salomonis N. DeepImmuno: deep learning-empowered prediction and generation of immunogenic peptides for T-cell immunity. Briefings in Bioinformatics [Internet]. 5 de noviembre de 2021 [citado 17 de noviembre de 2024];22(6):bbab160. Disponible en: https://academic.oup.com/bib/article/doi/10.1093/bib/bbab160/6261914

39. Larie D, An G, Cockrell C. Preparing for the next COVID: Deep Reinforcement Learning trained Artificial Intelligence discovery of multi-modal immunomodulatory control of systemic inflammation in the absence of effective anti-microbials [Internet]. Immunology; 2022 [citado 17 de noviembre de 2024]. Disponible en: http://biorxiv.org/lookup/doi/10.1101/2022.02.17.480940

40. Zhao AP, Li S, Cao Z, Hu PJH, Wang J, Xiang Y, et al. AI for science: Predicting infectious diseases. Journal of Safety Science and Resilience [Internet]. junio de 2024 [citado 17 de noviembre de 2024];5(2):130-46. Disponible en: https://linkinghub.elsevier.com/retrieve/pii/S266644962400015X

41. Ong E, Wong MU, Huffman A, He Y. COVID-19 Coronavirus Vaccine Design Using Reverse Vaccinology and Machine Learning. Front Immunol [Internet]. 3 de julio de 2020 [citado 17 de noviembre de 2024];11:1581. Disponible en: https://www.frontiersin.org/article/10.3389/fimmu.2020.01581/full

42. Goktas P, Karakaya G, Kalyoncu AF, Damadoglu E. Artificial Intelligence Chatbots in Allergy and Immunology Practice: Where Have We Been and Where Are We Going? The Journal of Allergy and Clinical Immunology: In Practice [Internet]. septiembre de 2023 [citado 16 de noviembre de 2024];11(9):2697-700. Disponible en: https://linkinghub.elsevier.com/retrieve/pii/S2213219823006414

43. Saadat A, Siddiqui T, Taseen S, Mughal S. Revolutionising Impacts of Artificial Intelligence on Health Care System and Its Related Medical In-Transparencies. Ann Biomed Eng [Internet]. junio de 2024 [citado 16 de noviembre de 2024];52(6):1546-8. Disponible en: https://link.springer.com/10.1007/s10439-023-03343-6

44. El Naqa I, Karolak A, Luo Y, Folio L, Tarhini AA, Rollison D, et al. Translation of AI into oncology clinical practice. Oncogene [Internet]. 13 de octubre de 2023 [citado 16 de noviembre de 2024];42(42):3089-97. Disponible en: https://www.nature.com/articles/s41388-023-02826-z

45. Clement J, Maldonado AQ. Augmenting the Transplant Team With Artificial Intelligence: Toward Meaningful AI Use in Solid Organ Transplant. Front Immunol [Internet]. 11 de junio de 2021 [citado 16 de noviembre de 2024];12:694222. Disponible en: https://www.frontiersin.org/articles/10.3389/fimmu.2021.694222/full

46. Singh B, Jevnikar AM, Desjardins E. Artificial Intelligence, Big Data, and Regulation of Immunity: Challenges and Opportunities. Archivum Immunologiae et Therapiae Experimentalis [Internet]. 1 de enero de 2024 [citado 16 de noviembre de 2024];72(1):20240006. Disponible en: https://www.sciendo.com/article/10.2478/aite-2024-0006

47. Bottomly D, McWeeney S. Just how transformative will AI/ML be for immuno-oncology? J Immunother Cancer [Internet]. marzo de 2024 [citado 16 de noviembre de 2024];12(3):e007841. Disponible en: https://jitc.bmj.com/lookup/doi/10.1136/jitc-2023-007841

Published

2025-11-22

How to Cite

1.
Vitón-Castillo AA, Miló-Valdés CA, Maldonado E, Pérez Acevedo LC. Application of Artificial intelligence on the field of immunology . Rev Cubana Inv Bioméd [Internet]. 2025 Nov. 22 [cited 2025 Nov. 24];44. Available from: https://revibiomedica.sld.cu/index.php/ibi/article/view/3580

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ARTÍCULOS DE REVISIÓN