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Review
Artificial intelligence in healthcare: past, present and future
  1. Fei Jiang1,
  2. Yong Jiang2,
  3. Hui Zhi3,
  4. Yi Dong4,
  5. Hao Li5,
  6. Sufeng Ma6,
  7. Yilong Wang7,
  8. Qiang Dong4,
  9. Haipeng Shen8,
  10. Yongjun Wang9
  1. 1 Department of Statistics and Actuarial Sciences, University of Hong Kong, Hong Kong, China
  2. 2 Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
  3. 3 Biostatistics and Clinical Research Methodology Unit, University of Hong Kong Li Ka Shing Faculty of Medicine, Hong Kong, China
  4. 4 Department of Neurology, Huashan Hospital, Fudan University, Shanghai, China
  5. 5 China National Clinical Research Center for Neurological Diseases, Beijing, China
  6. 6 DotHealth, Shanghai, China
  7. 7 Department of Neurology, Tiantan Clinical Trial and Research Center for Stroke, Beijing, China
  8. 8 Faculty of Business and Economics, University of Hong Kong, Hong Kong, China
  9. 9 Department of Neurology, Beijing Tiantan Hospital, Beijing, China
  1. Correspondence to Prof Yongjun Wang; yongjunwang1962{at}gmail.com

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.

  • big data
  • deep learning
  • neural network
  • support vector machine
  • stroke

This is an Open Access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/

https://doi.org/10.1136/svn-2017-000101

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    Footnotes

    • Competing interests None declared.

    • Provenance and peer review Commissioned; internally peer reviewed.

    • Data sharing statement No additional data are available.

    • Correction notice This paper has been corrected since it was published Online First. Owing to a scripting error, some of the publisher names in the references were replaced with ’BMJ Publishing Group'. This only affected the full text version, not the PDF. We have since corrected these errors and the correct publishers have been inserted into the references. Figures 6-9 have also been corrected.