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Affiliations

¹ Department of Statistics and Actuarial Sciences, University of Hong Kong, Hong Kong, China.

² Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China.

³ Biostatistics and Clinical Research Methodology Unit, University of Hong Kong Li Ka Shing Faculty of Medicine, Hong Kong, China.

⁴ Department of Neurology, Huashan Hospital, Fudan University, Shanghai, China.

⁵ China National Clinical Research Center for Neurological Diseases, Beijing, China.

⁶ DotHealth, Shanghai, China.

⁷ Department of Neurology, Tiantan Clinical Trial and Research Center for Stroke, Beijing, China.

⁸ Faculty of Business and Economics, University of Hong Kong, Hong Kong, China.

⁹ Department of Neurology, Beijing Tiantan Hospital, Beijing, China.

Free PMC article

Free PMC article

Affiliations

¹ Department of Statistics and Actuarial Sciences, University of Hong Kong, Hong Kong, China.

² Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China.

³ Biostatistics and Clinical Research Methodology Unit, University of Hong Kong Li Ka Shing Faculty of Medicine, Hong Kong, China.

⁴ Department of Neurology, Huashan Hospital, Fudan University, Shanghai, China.

⁵ China National Clinical Research Center for Neurological Diseases, Beijing, China.

⁶ DotHealth, Shanghai, China.

⁷ Department of Neurology, Tiantan Clinical Trial and Research Center for Stroke, Beijing, China.

⁸ Faculty of Business and Economics, University of Hong Kong, Hong Kong, China.

⁹ Department of Neurology, Beijing Tiantan Hospital, Beijing, China.

Abstract

Artificial intelligence (AI) aims to mimic human cognitive functions. It is bringing a paradigm shift to healthcare, powered by increasing availability of healthcare data and rapid progress of analytics techniques. We survey the current status of AI applications in healthcare and discuss its future. AI can be applied to various types of healthcare data (structured and unstructured). 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. Major disease areas that use AI tools include cancer, neurology and cardiology. 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. We conclude with discussion about pioneer AI systems, such as IBM Watson, and hurdles for real-life deployment of AI.

Keywords: big data; deep learning; neural network; stroke; support vector machine.

Conflict of interest statement

Competing interests: None declared.

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