APPLICATION OF ARTIFICIAL INTELLIGENCE TECHNOLOGIES IN RADIOLOGY: DIAGNOSTIC CAPABILITIES, ADVANTAGES AND CURRENT LIMITATIONS

The article discusses current applications of artificial intelligence technologies in radiology and diagnostic imaging. Particular attention is paid to the use of machine learning and deep learning algorithms in radiography, computed tomography, magnetic resonance imaging, ultrasound and other medical imaging modalities. Artificial intelligence systems can support automated detection of pathological findings, segmentation of anatomical struc­tures, quantitative assessment of detected lesions and differential diagnostic decision-making.

The principal advantages of these technologies include rapid processing of large volumes of imaging data, reduction of observer-related variability, improved reproducibility of diagnostic assessments and optimization of ra­diologists’ workflow. At the same time, several important limitations remain. These include dependence on the quality and representativeness of training datasets, the possibility of false-positive and false-negative findings, limited gen­eralizability of some algorithms across different institutions and imaging equipment, issues related to personal data protection and the need for thorough clinical validation.

Artificial intelligence should therefore be considered primarily as a decision-support tool that complements the knowledge and clinical experience of radiologists. Its effective implementation requires appropriate validation, responsible integration into clinical practice and continuous professional oversight.