ROLE OF ARTIFICIAL INTELLIGENCE IN AUTOMATIC ANALYSIS OF MEDICAL IMAGES: MRI, CT, X-RAY
Abstract
Medical imaging plays a pivotal role in diagnosis, treatment planning, and disease monitoring across a wide spectrum of clinical conditions. Conventional interpretation of MRI, CT, and X-ray images relies heavily on radiologist expertise, which is time-consuming, subject to human error, and influenced by inter-observer variability. Artificial Intelligence (AI), particularly machine learning (ML) and deep learning (DL) approaches, has emerged as a transformative tool for automatic analysis of medical images. This thesis examines the role of AI in enhancing diagnostic accuracy, efficiency, and predictive insights in MRI, CT, and X-ray imaging. Applications include automated detection of tumors, fractures, vascular abnormalities, and degenerative conditions. Challenges such as data privacy, algorithmic bias, and integration into clinical workflows are discussed, alongside future directions for AI-driven imaging. AI-driven analysis promises to optimize radiological workflows, reduce diagnostic errors, and improve patient-centered care.
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Copyright (c) 2025 Fazliddin Arziqulov, Sayfullayeva Dilbar Izzatillayevna, Maxsudov Valijon Gafurjonovich (Author)
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