Opportunities and challenges with artificial intelligence in allergy and immunology: a bibliometric study
IntroductionThe fields of allergy and immunology are increasingly recognizing the transformative potential of artificial intelligence (AI). Its adoption is reshaping research directions, clinical practices, and healthcare systems. However, a systematic overview identifying current statuses, emerging...
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Frontiers Media S.A.
2025-04-01
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| Series: | Frontiers in Medicine |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fmed.2025.1523902/full |
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| author | Ningkun Xiao Ningkun Xiao Xinlin Huang Yujun Wu Baoheng Li Wanli Zang Khyber Shinwari Khyber Shinwari Irina A. Tuzankina Valery A. Chereshnev Valery A. Chereshnev Guojun Liu |
| author_facet | Ningkun Xiao Ningkun Xiao Xinlin Huang Yujun Wu Baoheng Li Wanli Zang Khyber Shinwari Khyber Shinwari Irina A. Tuzankina Valery A. Chereshnev Valery A. Chereshnev Guojun Liu |
| author_sort | Ningkun Xiao |
| collection | DOAJ |
| description | IntroductionThe fields of allergy and immunology are increasingly recognizing the transformative potential of artificial intelligence (AI). Its adoption is reshaping research directions, clinical practices, and healthcare systems. However, a systematic overview identifying current statuses, emerging trends, and future research hotspots is lacking.MethodsThis study applied bibliometric analysis methods to systematically evaluate the global research landscape of AI applications in allergy and immunology. Data from 3,883 articles published by 21,552 authors across 1,247 journals were collected and analyzed to identify leading contributors, prevalent research themes, and collaboration patterns.ResultsAnalysis revealed that the USA and China are currently leading in research output and scientific impact in this domain. AI methodologies, especially machine learning (ML) and deep learning (DL), are predominantly applied in drug discovery and development, disease classification and prediction, immune response modeling, clinical decision support, diagnostics, healthcare system digitalization, and medical education. Emerging trends indicate significant movement toward personalized medical systems integration.DiscussionThe findings demonstrate the dynamic evolution of AI in allergy and immunology, highlighting the broadening scope from basic diagnostics to comprehensive personalized healthcare systems. Despite advancements, critical challenges persist, including technological limitations, ethical concerns, and regulatory frameworks that could potentially hinder further implementation and integration.ConclusionAI holds considerable promise for advancing allergy and immunology globally by enhancing healthcare precision, efficiency, and accessibility. Addressing existing technological, ethical, and regulatory challenges will be crucial to fully realizing its potential, ultimately improving global health outcomes and patient well-being. |
| format | Article |
| id | doaj-art-c540764d3df5413a85a642055304a606 |
| institution | DOAJ |
| issn | 2296-858X |
| language | English |
| publishDate | 2025-04-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Medicine |
| spelling | doaj-art-c540764d3df5413a85a642055304a6062025-08-20T03:05:13ZengFrontiers Media S.A.Frontiers in Medicine2296-858X2025-04-011210.3389/fmed.2025.15239021523902Opportunities and challenges with artificial intelligence in allergy and immunology: a bibliometric studyNingkun Xiao0Ningkun Xiao1Xinlin Huang2Yujun Wu3Baoheng Li4Wanli Zang5Khyber Shinwari6Khyber Shinwari7Irina A. Tuzankina8Valery A. Chereshnev9Valery A. Chereshnev10Guojun Liu11Department of Immunochemistry, Institution of Chemical Engineering, Ural Federal University, Yekaterinburg, RussiaLaboratory for Brain and Neurocognitive Development, Department of Psychology, Institution of Humanities, Ural Federal University, Yekaterinburg, RussiaLaboratory for Brain and Neurocognitive Development, Department of Psychology, Institution of Humanities, Ural Federal University, Yekaterinburg, RussiaPreventive Medicine and Software Engineering, West China School of Public Health, Sichuan University, Chengdu, ChinaEngineering School of Information Technologies, Telecommunications and Control Systems, Ural Federal University, Yekaterinburg, RussiaPostgraduate School, University of Harbin Sport, Harbin, ChinaLaboratório de Biologia Molecular de Microrganismos, Universidade São Francisco, Bragança Paulista, BrazilDepartment of Biology, Nangrahar University, Nangrahar, AfghanistanInstitute of Immunology and Physiology of the Ural Branch of the Russian Academy of Sciences, Yekaterinburg, RussiaDepartment of Immunochemistry, Institution of Chemical Engineering, Ural Federal University, Yekaterinburg, RussiaInstitute of Immunology and Physiology of the Ural Branch of the Russian Academy of Sciences, Yekaterinburg, RussiaSchool of Life Science and Technology, Inner Mongolia University of Science and Technology, Baotou, ChinaIntroductionThe fields of allergy and immunology are increasingly recognizing the transformative potential of artificial intelligence (AI). Its adoption is reshaping research directions, clinical practices, and healthcare systems. However, a systematic overview identifying current statuses, emerging trends, and future research hotspots is lacking.MethodsThis study applied bibliometric analysis methods to systematically evaluate the global research landscape of AI applications in allergy and immunology. Data from 3,883 articles published by 21,552 authors across 1,247 journals were collected and analyzed to identify leading contributors, prevalent research themes, and collaboration patterns.ResultsAnalysis revealed that the USA and China are currently leading in research output and scientific impact in this domain. AI methodologies, especially machine learning (ML) and deep learning (DL), are predominantly applied in drug discovery and development, disease classification and prediction, immune response modeling, clinical decision support, diagnostics, healthcare system digitalization, and medical education. Emerging trends indicate significant movement toward personalized medical systems integration.DiscussionThe findings demonstrate the dynamic evolution of AI in allergy and immunology, highlighting the broadening scope from basic diagnostics to comprehensive personalized healthcare systems. Despite advancements, critical challenges persist, including technological limitations, ethical concerns, and regulatory frameworks that could potentially hinder further implementation and integration.ConclusionAI holds considerable promise for advancing allergy and immunology globally by enhancing healthcare precision, efficiency, and accessibility. Addressing existing technological, ethical, and regulatory challenges will be crucial to fully realizing its potential, ultimately improving global health outcomes and patient well-being.https://www.frontiersin.org/articles/10.3389/fmed.2025.1523902/fullartificial intelligenceallergy and immunologyimmunologymachine learningdeep learninghealth management |
| spellingShingle | Ningkun Xiao Ningkun Xiao Xinlin Huang Yujun Wu Baoheng Li Wanli Zang Khyber Shinwari Khyber Shinwari Irina A. Tuzankina Valery A. Chereshnev Valery A. Chereshnev Guojun Liu Opportunities and challenges with artificial intelligence in allergy and immunology: a bibliometric study Frontiers in Medicine artificial intelligence allergy and immunology immunology machine learning deep learning health management |
| title | Opportunities and challenges with artificial intelligence in allergy and immunology: a bibliometric study |
| title_full | Opportunities and challenges with artificial intelligence in allergy and immunology: a bibliometric study |
| title_fullStr | Opportunities and challenges with artificial intelligence in allergy and immunology: a bibliometric study |
| title_full_unstemmed | Opportunities and challenges with artificial intelligence in allergy and immunology: a bibliometric study |
| title_short | Opportunities and challenges with artificial intelligence in allergy and immunology: a bibliometric study |
| title_sort | opportunities and challenges with artificial intelligence in allergy and immunology a bibliometric study |
| topic | artificial intelligence allergy and immunology immunology machine learning deep learning health management |
| url | https://www.frontiersin.org/articles/10.3389/fmed.2025.1523902/full |
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