ARTIFICIAL INTELLIGENCE IN DISEASE DIAGNOSIS: POTENTIAL AND LIMITATIONS IN CLINICAL PRACTICE

Artificial intelligence (AI) is rapidly transforming medical diagnostics, offering fundamentally new approaches to clinical data analysis. This article, based on an analysis of current scientific publications, examines the potential and limitations of implementing AI systems in clinical practice. Advances in medical imaging, disease prognosis, and clinical decision support are analyzed, as well as critical challenges related to the methodological quality of studies, model interpretability, integration into clinical workflows, and ethical aspects. Particular attention is paid to the challenge of transitioning from demonstrating technical effectiveness to ensuring clinical significance and reproducibility of results. A conclusion is drawn regarding the need for a research paradigm shift: from comparative "AI versus physician" trials to the development of standardized methodological approaches and the creation of explainable, clinically oriented systems.