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Artificial intelligence in healthcare: past, present and future

Fei JiangDepartment of Statistics and Actuarial Sciences, University of Hong Kong, Hong Kong, ChinaYong JiangBeijing Tian Tan HospitalHui ZhiBiostatistics and Clinical Research Methodology Unit, University of Hong Kong Li Ka Shing Faculty of Medicine, Hong Kong, ChinaYi DongHuashan HospitalHao LiChina National Clinical Research Center for Neurological Diseases, Beijing, ChinaSufeng MaYilong WangDepartment of Neurology, Beijing Tiantan Hospital, Beijing, ChinaYilong WangDepartment of Neurology, Tiantan Clinical Trial and Research Center for Stroke, Beijing, ChinaQiang DongUniversity of Hong KongHaipeng ShenDepartment of Neurology, Beijing Tiantan Hospital, Beijing, ChinaYongjun WangDepartment of Neurology, Beijing Tiantan Hospital, Beijing, China

2017en

ABI

Annotatsiya

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.

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Identifikatorlar

DOI: 10.1136/svn-2017-000101

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