Аннотация
Artificial intelligence is rapidly transforming the field of rheumatology by enhancing the accuracy of early diagnosis, improving disease monitoring, and supporting personalized treatment strategies for patients with chronic rheumatic diseases. The increasing availability of electronic health records, advanced imaging modalities, laboratory biomarkers, and wearable technologies has created unprecedented opportunities for the implementation of machine learning (ML), deep learning (DL), and natural language processing (NLP) in routine clinical practice. AI algorithms have demonstrated remarkable performance in detecting early inflammatory arthritis, differentiating autoimmune diseases, predicting disease progression, and estimating treatment response before irreversible structural damage occurs. Furthermore, AI-assisted analysis of musculoskeletal ultrasound, magnetic resonance imaging, and conventional radiographs enables earlier recognition of inflammatory changes while reducing observer variability. Continuous monitoring using digital health technologies and wearable devices further facilitates real-time assessment of disease activity, physical function, and patient-reported outcomes. Predictive analytics also assists clinicians in identifying patients at increased risk of disease flares, medication failure, or adverse events, thereby enabling more individualized therapeutic decisions. Despite these advances, several challenges remain, including limited external validation, algorithm transparency, data privacy concerns, potential biases within training datasets, and the need for international regulatory standards governing AI implementation in healthcare. Future developments are expected to integrate multimodal datasets incorporating clinical, imaging, genomic, proteomic, and digital biomarkers into comprehensive AI-driven decision support systems capable of optimizing precision rheumatology. This review summarizes current applications of artificial intelligence in rheumatology, highlights recent technological advances, discusses existing limitations, and explores future perspectives for improving early diagnosis, disease monitoring, and personalized patient care.