Artificial intelligence in healthcare: past, present and future

Искусственный интеллект в здравоохранении: прошлое, настоящее и будущее
Yongjun Wang, Yongjun Wang, Qiang Dong, Hao Li, Yong Jiang, Fei Jiang, Hui Zhi, Yi Dong, Sufeng Ma, Yilong Wang, Haipeng Shen, Yilong Wang
2017-06-21

IBM Watsonartificial intelligence in healthcaredeep learninghealthcare data (structured and unstructured)machine learningnatural language processingneural networkstroke detection and diagnosissupport vector machinetreatment and outcome prediction
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.
1
AI applies to both structured and unstructured healthcare data using methods like classical machine learning (SVM, neural networks), modern deep learning, and NLP.
2
AI is driving a paradigm shift in healthcare due to increased availability of healthcare data and advances in analytics techniques.
3
In stroke care, AI has applications across early detection and diagnosis, treatment, and outcome prediction and prognosis evaluation.
4
Major disease areas utilizing AI tools include cancer, neurology, and cardiology.
5
Real-life deployment of AI faces hurdles despite pioneer systems such as IBM Watson.

Artificial intelligence applications in healthcare (for structured and unstructured healthcare data)

Current status, use-cases and future directions of AI techniques (machine learning, deep learning, NLP) across healthcare tasks—early detection/diagnosis, treatment, outcome prediction/prognosis—with discussion of deployment hurdles and pioneer systems

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2017-06-21
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Yongjun Wang
Yongjun Wang
Qiang Dong
Hao Li
Yong Jiang
Fei Jiang
Hui Zhi
Yi Dong
Sufeng Ma
Yilong Wang
Haipeng Shen
Yilong Wang
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