In this article, we survey the current status of AI in healthcare, as well as discuss its future. Guided by relevant clinical questions, powerful AI techniques can unlock clinically relevant information hidden in the massive amount of data, which in turn can assist clinical decision making. The increasing availability of healthcare data and rapid development of big data analytic methods has made possible the recent successful applications of AI in healthcare. We believe that human physicians will not be replaced by machines in the foreseeable future, but AI can definitely assist physicians to make better clinical decisions or even replace human judgement in certain functional areas of healthcare (eg, radiology). Recently AI techniques have sent vast waves across healthcare, even fuelling an active discussion of whether AI doctors will eventually replace human physicians in the future. Overview of the medical artificial intelligence (AI) research We conclude with discussion about pioneer AI systems, such as IBM Watson, and hurdles for real-life deployment of AI. We then review in more details the AI applications in stroke, in the three major areas of early detection and diagnosis, treatment, as well as outcome prediction and prognosis evaluation. Major disease areas that use AI tools include cancer, neurology and cardiology. Popular AI techniques include machine learning methods for structured data, such as the classical support vector machine and neural network, and the modern deep learning, as well as natural language processing for unstructured data. AI can be applied to various types of healthcare data (structured and unstructured). We survey the current status of AI applications in healthcare and discuss its future. It is bringing a paradigm shift to healthcare, powered by increasing availability of healthcare data and rapid progress of analytics techniques. Artificial intelligence (AI) aims to mimic human cognitive functions.
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