Artificial intelligence (AI) is transforming healthcare by enabling better diagnosis, treatment, and prediction of diseases. This review discusses the current state and future of AI in healthcare, focusing on its applications in cancer, neurology, and cardiology. AI techniques, including machine learning (ML) and natural language processing (NLP), are used to analyze structured and unstructured healthcare data. ML methods such as support vector machines (SVM) and neural networks are applied to structured data like imaging, genetic, and electrophysiological data, while NLP is used to extract information from unstructured clinical texts. AI has shown promise in stroke care, particularly in early detection, diagnosis, treatment, and prognosis evaluation. For example, SVM and neural networks have been used to identify stroke-related patterns in imaging data, while NLP helps extract relevant information from clinical notes. Deep learning, a modern extension of neural networks, has also been applied to medical imaging, achieving high accuracy in diagnosing conditions like skin cancer and diabetic retinopathy. Despite its potential, AI faces challenges in real-world implementation, including regulatory hurdles and data sharing issues. The IBM Watson system is an example of a pioneering AI tool in oncology. The future of AI in healthcare depends on overcoming these challenges and integrating AI into clinical practice to improve patient outcomes.Artificial intelligence (AI) is transforming healthcare by enabling better diagnosis, treatment, and prediction of diseases. This review discusses the current state and future of AI in healthcare, focusing on its applications in cancer, neurology, and cardiology. AI techniques, including machine learning (ML) and natural language processing (NLP), are used to analyze structured and unstructured healthcare data. ML methods such as support vector machines (SVM) and neural networks are applied to structured data like imaging, genetic, and electrophysiological data, while NLP is used to extract information from unstructured clinical texts. AI has shown promise in stroke care, particularly in early detection, diagnosis, treatment, and prognosis evaluation. For example, SVM and neural networks have been used to identify stroke-related patterns in imaging data, while NLP helps extract relevant information from clinical notes. Deep learning, a modern extension of neural networks, has also been applied to medical imaging, achieving high accuracy in diagnosing conditions like skin cancer and diabetic retinopathy. Despite its potential, AI faces challenges in real-world implementation, including regulatory hurdles and data sharing issues. The IBM Watson system is an example of a pioneering AI tool in oncology. The future of AI in healthcare depends on overcoming these challenges and integrating AI into clinical practice to improve patient outcomes.