<![CDATA[Artificial intelligence (AI) and machine learning (ML) are reshaping healthcare by supporting faster diagnosis, predictive modeling, and efficient clinical workflows. This review examines 52 recent studies to assess how these technologies are applied across diagnostics, predictive analytics, patient monitoring, operations, treatment, and ethical considerations. Results show substantial progress in imaging, genomics, drug discovery, and hospital management, where systems often match or surpass human performance. At the same time, challenges such as limited generalizability, data bias, privacy concerns, and lack of interpretability remain significant barriers to adoption. This review identifies common strengths and gaps by grouping existing work into six themes, offering a structured view of current developments. The findings suggest that the future of AI in medical care lies in transparent, fair, and clinically validated systems that can scale across diverse populations and settings.]]>
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