Background: Artificial intelligence (AI) is transforming healthcare by advancing diagnostic accuracy, optimizing treatment pathways, and enabling personalized medicine and efficient administrative processes. Despite these advancements, its adoption raises ethical, legal, and operational challenges. This review critically examines AI’s dual impact on healthcare by assessing its applications in diagnostics, robotic-assisted surgery, predictive analytics, drug discovery, and telemedicine while addressing key concerns like data privacy, algorithmic bias, workforce implications, and technological dependency. Methodology: A systematic review of peer-reviewed literature published between 2018 and 2024 was conducted using databases such as PubMed, Web of Science, and Google Scholar. Search terms included "artificial intelligence in clinical practice," "machine learning ethics in medicine," "AI-driven diagnostics," and "healthcare predictive analytics." Articles were selected based on their relevance to AI's clinical applications and ethical implications, emphasizing qualitative evaluations and real-world case studies in diagnostic imaging, minimally invasive surgery, pharmacological innovation, and virtual healthcare delivery.

Results: AI has shown significant promise in enhancing healthcare outcomes, particularly in achieving diagnostic accuracies on par with or exceeding human expertise in imaging and pathology. It has accelerated drug development pipelines, improved surgical precision, and streamlined clinical workflows. However, the review highlights persistent challenges, including algorithmic bias, insufficient transparency in AI systems, and ethical concerns regarding patient data security. The findings also underscore risks of workforce displacement and over-reliance on automated systems in decision-making processes.

Conclusion: AI is poised to revolutionize healthcare, yet its integration must be carefully managed to address ethical and practical concerns. Ensuring that AI systems are transparent, unbiased, and designed to augment—rather than replace—clinical expertise is essential. Future progress should prioritize ethical AI design, rigorous regulatory frameworks, and fostering synergy between AI technologies and healthcare professionals to maximize patient outcomes and system efficiency.

Keywords: Artificial intelligence (AI) in medicine, Ethical challenges of AI, AI-driven diagnostics, Predictive analytics in healthcare, AI applications in drug discovery

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