Artificial intelligence in healthcare: past, present and future
Fei Jianga, Yong Jiangb, Hui Zhic, Yi Dongd, Hao Lie, Sufeng Maf, Yilong Wangg h, Yilong Wangd g h✉, Qiang Dongd i, Haipeng Sheng i, Yongjun Wangg h✉, Yongjun Wangg h
- aDepartment of Statistics and Actuarial Sciences, University of Hong Kong, Hong Kong, China
- bDepartment of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
- cBiostatistics and Clinical Research Methodology Unit, University of Hong Kong Li Ka Shing Faculty of Medicine, Hong Kong, China
- dDepartment of Neurology, Huashan Hospital, Fudan University, Shanghai, China
- eChina National Clinical Research Center for Neurological Diseases, Beijing, China
- fDotHealth, Shanghai, China
- gDepartment of Neurology, Beijing Tiantan Hospital, Beijing, China
- hDepartment of Neurology, Tiantan Clinical Trial and Research Center for Stroke, Beijing, China
- iFaculty of Business and Economics, University of Hong Kong, Hong Kong, China
- ✉Corresponding author
Papers open inside SciRah from the original publisher or repository; SciRah never hosts them.
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.
TL;DRThe current status of AI applications in healthcare, in the three major areas of early detection and diagnosis, treatment, as well as outcome prediction and prognosis evaluation, are surveyed and its future is discussed. (Semantic Scholar)