<![CDATA[Purpose: This systematic review critically evaluates recent advances in AI applied to MRI image analysis for radiological diagnosis, emphasizing improvements in diagnostic accuracy and clinical utility.Methodology: A systematic literature review (SLR) was conducted using PRISMA guidelines, employing a PICOC framework. A comprehensive search of the Scopus database was performed, and studies were selected based on strict inclusion/exclusion criteria through screening and synthesis.Findings: The review found that AI techniques significantly enhance MRI diagnostic performance (e.g., better tumor detection) and streamline workflows by automating routine tasks. It also notes growing publication trends from 2020–2024 in this field, reflecting increasing global research interest.Research Limitations: The review is limited by its reliance on a single database (Scopus) and a narrow publication window (2020–2024). Many included studies exhibit data biases and lack comprehensive external validation, which may affect generalizability.Practical Implications: These results suggest that AI integration can improve clinical workflows. The authors emphasize the need for standardized protocols and multidisciplinary collaboration to ensure safe and effective implementation of AI in radiological practice.Originality: This study provides an original contribution by systematically synthesizing the latest literature on AI applications in MRI diagnostics, offering a comprehensive overview of current methods and trends. It fills a gap by critically evaluating recent studies and outlining future research directions.]]>
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