Artificial intelligence in healthcare: past, present and future
Искусственный интеллект в здравоохранении: прошлое, настоящее и будущее
2017-06-21
SCID: 54.1/qvh9rtwu
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IBM Watsonartificial intelligence in healthcaredeep learninghealthcare data (structured and unstructured)machine learningnatural language processingneural networkstroke detection and diagnosissupport vector machinetreatment and outcome prediction
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Abstract (AI)
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.
Key Findings
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.
Research Object
Artificial intelligence applications in healthcare (for structured and unstructured healthcare data)
Research Subject
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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