Artificial intelligence (AI) has emerged as a transformative tool across multiple sectors, with healthcare being one of the most promising domains. This review article explores the foundational concepts of AI and its rapidly expanding applications in the healthcare sector. The integration of AI in health systems encompasses various branches, including diagnostic imaging, drug discovery, virtual health assistants, robotic surgery, and personalized medicine. AI-powered tools have demonstrated significant advantages, such as enhancing diagnostic accuracy, optimizing treatment plans, reducing administrative burdens, and improving patient outcomes. However, the deployment of AI in healthcare also presents notable challenges and limitations. These include data privacy concerns, algorithmic bias, lack of transparency, and the need for substantial infrastructure and workforce training. Moreover, ethical and regulatory issues continue to influence the pace and scope of AI adoption. This review critically examines these aspects while highlighting recent innovations that underscore AIs potential. Finally, the article outlines future directions for AI in healthcare, emphasizing the need for interdisciplinary collaboration, robust ethical frameworks, and the development of explainable AI systems. As technology evolves, a balanced approach that maximizes benefits while mitigating risks is essential for the sustainable integration of AI into global health systems.
Islam et al. (2025) studied this question.