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This article explores the evolving landscape of artificial intelligence in healthcare, focusing on recent advances in diagnostic accuracy, predictive analytics, and clinical decision support systems. It examines real-world implementations across radiology, pathology, and chronic disease management, highlighting improved patient outcomes and operational efficiencies. The piece also addresses critical challenges—including data privacy concerns, algorithmic bias, regulatory hurdles, and clinician adoption barriers—while emphasizing the necessity of human-AI collaboration over full automation. Ethical considerations, interoperability standards, and the role of multidisciplinary teams in responsible AI integration are discussed. Supported by case studies from leading academic medical centers and FDA-cleared tools, the article advocates for evidence-based, equity-centered AI deployment to augment—not replace—healthcare professionals.