Journal Press India^®

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The integration of artificial intelligence (AI) and machine learning (ML) technologies in healthcare has revolutionized clinical decision-making, diagnostic accuracy, and treatment planning. This comprehensive review examines the current state of AI/ML applications across multiple healthcare domains, including medical imaging, drug discovery, clinical prediction models, and personalized medicine. Through systematic analysis of 40+ peer-reviewed studies and clinical trials, we evaluate the clinical efficacy, regulatory challenges, ethical considerations, and implementation barriers associated with these technologies. Our findings demonstrate that while AI/ML systems have achieved performance metrics comparable to or exceeding human experts in specific diagnostic tasks, significant gaps remain in generalizability, interpretability, and clinical validation. This paper synthesizes current evidence on AI/ML applications, discusses critical challenges in model validation and regulatory approval, addresses ethical concerns regarding bias and patient privacy, and proposes a framework for responsible AI implementation in clinical practice. We conclude that successful integration of AI/ML in healthcare requires interdisciplinary collaboration between clinicians, computer scientists, bioethicists, and regulatory bodies to ensure patient safety, equitable access, and evidence-based clinical practice.

Keywords

Artificial intelligence, Machine learning, Healthcare, Clinical applications, Deep learning, Drug discovery, Medical imaging, Precision medicine, Regulatory challenges, Clinical validation

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