The article provides a comprehensive overview of the current status and future prospects of artificial intelligence (AI) in healthcare. AI, which aims to mimic human cognitive functions, is revolutionizing healthcare through the increasing availability of healthcare data and rapid advancements in analytics techniques. The authors discuss the application of AI in various types of healthcare data, including structured and unstructured data, and highlight popular AI techniques such as machine learning (ML) and natural language processing (NLP). Key disease areas where AI tools are being used include cancer, neurology, and cardiology. The article delves into the specific applications of AI in stroke care, focusing on early detection and diagnosis, treatment, and outcome prediction. It also reviews the pioneering AI systems like IBM Watson and addresses the challenges and hurdles in real-life deployment of AI in healthcare, including regulatory issues and data exchange. The authors conclude by emphasizing the potential of AI to enhance clinical decision-making and improve patient outcomes.The article provides a comprehensive overview of the current status and future prospects of artificial intelligence (AI) in healthcare. AI, which aims to mimic human cognitive functions, is revolutionizing healthcare through the increasing availability of healthcare data and rapid advancements in analytics techniques. The authors discuss the application of AI in various types of healthcare data, including structured and unstructured data, and highlight popular AI techniques such as machine learning (ML) and natural language processing (NLP). Key disease areas where AI tools are being used include cancer, neurology, and cardiology. The article delves into the specific applications of AI in stroke care, focusing on early detection and diagnosis, treatment, and outcome prediction. It also reviews the pioneering AI systems like IBM Watson and addresses the challenges and hurdles in real-life deployment of AI in healthcare, including regulatory issues and data exchange. The authors conclude by emphasizing the potential of AI to enhance clinical decision-making and improve patient outcomes.