ARTIFICIAL INTELLIGENCE IN THE DIAGNOSIS OF INBORN ERRORS OF IMMUNITY: CURRENT CAPABILITIES, CHALLENGES, AND FUTURE PERSPECTIVES - Boyarchuk, Oksana (Eastern Ukrainian Medical Journal, 2025)
Introduction. Inborn errors of immunity (IEI), also known as primary immunodeficiencies, comprise a heterogeneous group of rare genetic disorders with a wide range of clinical manifestations, including increased susceptibility to infections, autoimmune and inflammatory conditions, allergic diseases, and malignancies. Diagnosing these conditions is often challenging due to the nonspecific nature of symptoms, limited access to molecular genetic testing, and low clinical awareness among healthcare providers. In this context, the potential of artificial intelligence (AI) to improve the diagnosis of IEI is gaining increasing attention. The aim of this review is to analyze current and potential applications of AI in the diagnosis of IEI and to discuss the challenges of implementing such technologies in Ukrainian clinical practice. Materials and Methods. A comprehensive analysis of scientific literature was conducted, focusing on the use of AI and machine learning (ML) in the diagnosis of IEI and data-driven clinical decision-making. Sources were searched using databases such as PubMed, Scopus, and Web of Science. Results. The article outlines the primary areas of AI application in IEI diagnostics, including automated processing of clinical data, analysis of medical images, interpretation of next-generation sequencing (NGS) data, predictive modeling, and development of electronic decision-support systems. AI has been shown to reduce the time to diagnosis, decrease the number of unnecessary tests, standardize clinical approaches, and enhance access to personalized medicine. Current and emerging directions of AI use in IEI diagnosis are considered in light of ethical, technological, and practical aspects. Several successful cases of AI implementation in IEI diagnostics and clinical decision-making are presented. Key barriers to AI implementation in Ukraine are also highlighted, including insufficient digitalization of healthcare institutions, data fragmentation, lack of anonymized clinical datasets, ethical and legal concerns, and the need for interdisciplinary training of specialists. Conclusions. Artificial intelligence is a promising tool in the diagnosis of IEI, offering the potential for more accurate, timely, and cost-effective identification of these rare conditions. Its effective use requires a national digital health strategy, international collaboration, and the development of localized tools tailored to the Ukrainian healthcare context.