UTILIZATION OF ARTIFICIAL INTELLIGENCE CAPABILITIES IN IMMUNOLOGY: FROM LITERATURE REVIEW TO STATISTICAL PROCESSING AND ANALYSIS OF OBTAINED DATA



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Abstract

Abstract

Introduction. Traditional literature review and the use of standard data analysis methods in immunology require significant time and do not always allow researchers to cope with the scale and complexity of the problem. Artificial intelligence can assist in addressing this issue by automating the processes of searching for and evaluating relevant information. The aim of this study was to determine the potential of artificial intelligence in immunological research, focusing on the automation of literature data collection and the analysis of digital information. Materials and methods. The theoretical part of the study was based on the analysis of materials from the PubMed, Scopus, ResearchGate databases covering the years 2000–2025. The practical part of the study was conducted as a comparative analysis of previously studied data and conclusions obtained using artificial intelligence technologies, exemplified by GPT v.4.0 models. Results and discussion. It was found that artificial intelligence technologies can be useful in conducting literature reviews in immunology, including summarizing findings, generating graphical representations, and visualizing new signaling pathways, cellular interactions, and disease-related factors. In addition to text analysis, artificial intelligence can be applied to statistical processing and digital data analysis, such as identifying patterns, solving forecasting problems, building models. A comparison of previously studied data with the results obtained using GPT v.4.0 revealed several limitations of different chatbot models, including the dependence of responses on query formulation style, overly generalized information synthesis, limited text output (up to 1,000 words), plagiarism risks, difficulties in generating figures, diagrams, and tables, the predominant presentation of comprehensive information in English, and the creation of non-existent references to literary sources. Conclusion.  1. Artificial intelligence can transform immunological research, allowing for a more effective literature review and deeper data analysis, but expert supervision is required at the initial stages. 2. As artificial intelligence technologies develop, they will become an integral part of the immunologist's toolkit.

About the authors

Olga V. Berdiugina

Institute of Immunology and Physiology UrB RAS, Ekaterinburg, Russia

Author for correspondence.
Email: berolga73@rambler.ru
ORCID iD: 0000-0003-3479-9730
SPIN-code: 5230-7435
Scopus Author ID: 6504489044
ResearcherId: O-1102-2019

Ph.D., Dr.Sci. (Biol), Lead Researcher of the Inflammation Immunology Laboratory

Russian Federation, 106 Pervomayskaya St., Yekaterinburg, Russia, 620049

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