MODERN DIAGNOSTIC STRATEGIES IN AUTOIMMUNE DISEASES

Background: Autoimmune diseases constitute a heterogeneous group of disorders characterized by immune-mediated damage to self-tissues. Their prevalence has increased over recent decades, and early diagnosis remains challenging due to heterogeneous clinical presentations and overlapping symptoms. Advances in immunology and molecular biology have significantly transformed diagnostic approaches in autoimmune diseases. Aim: This review discusses modern diagnostic strategies used in autoimmune diseases. It presents emerging serological, molecular, and multi-parameter diagnostic tools, as well as the role of personalized diagnostics and advanced data analysis methods in improving diagnostic accuracy . Methods: The review was conducted using the PubMed, Web of Science, and Google Scholar databases, limited to full-text, open-access publications published between 2013 and 2025. Results: Recent studies indicate that novel diagnostic approaches, including next-generation autoantibody profiling, molecular biomarkers, “omics”- based technologies, and advanced imaging methods, improve early disease detection and patient stratification. The integration of artificial intelligence and machine learning algorithms enhances the interpretation of complex clinical and laboratory data, increasing diagnostic sensitivity and specificity. Conclusion: Modern diagnostic strategies significantly improve the early recognition and monitoring of autoimmune diseases. Integrating classical diagnostic methods with advanced molecular and computational tools supports the development of personalized medicine and may lead to better clinical outcomes in patients with autoimmune disorders.