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The implementation of artificial intelligence (AI) technologies is one of the most significant trends in modern healthcare, with the potential to fundamentally transform all levels of medical care. This article aims to comprehensively analyze current AI applications in diagnosis, treatment, prognosis, and organization of medical care, as well as to identify key ethical, legal, and organizational barriers to its integration. The methodological basis consists of a systems analysis and review of current scientific publications, clinical guidelines, and regulatory documents. The study identified key areas: supporting diagnostic decisions based on medical image analysis (computed tomography, magnetic resonance imaging, histology), developing personalized treatment regimens using predictive models, managing patient flow, and optimizing logistics in medical institutions. Particular attention is paid to predictive analytics systems for identifying at-risk patients and disease prevention. The article demonstrates that the main challenges remain data privacy and security, algorithmic bias, the need to validate algorithms in real-world clinical practice, and the lack of digital literacy among healthcare professionals. It concludes that successful AI integration requires not only technological development but also the creation of an adaptive regulatory framework, ethical standards, and a revision of educational programs to train a new generation of medical specialists – "digital doctors." Prospects lie in the development of multimodal systems that integrate data of various types (genomic, clinical, behavioral), enabling the implementation of a truly personalized and preventive model of medicine.
artificial intelligence, machine learning, healthcare, digital medicine, diagnostics, personalized medicine, predictive analytics, AI ethics, healthcare
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