Applications and Implications of Artificial Intelligence in Healthcare: A Comprehensive Review

Artificial Intelligence (AI) is transforming healthcare by supporting clinical decision-making, medical image analysis, disease prediction, personalized treatment, drug discovery,and remote patient monitoring. Machine learning, deep learning,natural language processing, and computer vision enable healthcare systems to extract useful information from medical images,electronic health records, laboratory reports, and physiologicalsignals. These capabilities can assist healthcare professionals inidentifying potential abnormalities, estimating clinical risks, andimproving the organization of patient care. However, the practicaladoption of AI introduces important challenges, including dataprivacy, algorithmic bias, limited interpretability, cybersecuritythreats, regulatory compliance, and the need for clinical validation. An AI system that performs well on a development datasetmay not achieve the same performance in a different hospitalor patient population. Therefore, accuracy alone is insufficientto establish clinical usefulness. This paper reviews the majorAI technologies used in healthcare, presents a generalized architecture for AI-enabled clinical systems, examines representativeapplications, and discusses their benefits, limitations, and ethicalimplications. It also explores future directions involving explainable AI, federated learning, multimodal models, and human–AIcollaboration. The review emphasizes that AI should complementclinical expertise rather than replace professional judgment.