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      Artificial intelligence in healthcare: past, present and future

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          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.

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          Gradient-based learning applied to document recognition

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            Dermatologist-level classification of skin cancer with deep neural networks

            Skin cancer, the most common human malignancy, is primarily diagnosed visually, beginning with an initial clinical screening and followed potentially by dermoscopic analysis, a biopsy and histopathological examination. Automated classification of skin lesions using images is a challenging task owing to the fine-grained variability in the appearance of skin lesions. Deep convolutional neural networks (CNNs) show potential for general and highly variable tasks across many fine-grained object categories. Here we demonstrate classification of skin lesions using a single CNN, trained end-to-end from images directly, using only pixels and disease labels as inputs. We train a CNN using a dataset of 129,450 clinical images—two orders of magnitude larger than previous datasets—consisting of 2,032 different diseases. We test its performance against 21 board-certified dermatologists on biopsy-proven clinical images with two critical binary classification use cases: keratinocyte carcinomas versus benign seborrheic keratoses; and malignant melanomas versus benign nevi. The first case represents the identification of the most common cancers, the second represents the identification of the deadliest skin cancer. The CNN achieves performance on par with all tested experts across both tasks, demonstrating an artificial intelligence capable of classifying skin cancer with a level of competence comparable to dermatologists. Outfitted with deep neural networks, mobile devices can potentially extend the reach of dermatologists outside of the clinic. It is projected that 6.3 billion smartphone subscriptions will exist by the year 2021 (ref. 13) and can therefore potentially provide low-cost universal access to vital diagnostic care.
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              Radiomics: Images Are More than Pictures, They Are Data

              This report describes the process of radiomics, its challenges, and its potential power to facilitate better clinical decision making, particularly in the care of patients with cancer.
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                Author and article information

                Journal
                Stroke Vasc Neurol
                Stroke Vasc Neurol
                svnbmj
                svn
                Stroke and Vascular Neurology
                BMJ Publishing Group (BMA House, Tavistock Square, London, WC1H 9JR )
                2059-8696
                December 2017
                21 June 2017
                : 2
                : 4
                : 230-243
                Affiliations
                [1 ] departmentDepartment of Statistics and Actuarial Sciences , University of Hong Kong , Hong Kong, China
                [2 ] departmentDepartment of Neurology , Beijing Tiantan Hospital, Capital Medical University , Beijing, China
                [3 ] departmentBiostatistics and Clinical Research Methodology Unit , University of Hong Kong Li Ka Shing Faculty of Medicine , Hong Kong, China
                [4 ] departmentDepartment of Neurology , Huashan Hospital, Fudan University , Shanghai, China
                [5 ] China National Clinical Research Center for Neurological Diseases , Beijing, China
                [6 ] DotHealth , Shanghai, China
                [7 ] departmentDepartment of Neurology , Tiantan Clinical Trial and Research Center for Stroke , Beijing, China
                [8 ] departmentFaculty of Business and Economics , University of Hong Kong , Hong Kong, China
                [9 ] departmentDepartment of Neurology , Beijing Tiantan Hospital , Beijing, China
                Author notes
                [Correspondence to ] Prof Yongjun Wang; yongjunwang1962@ 123456gmail.com
                Article
                svn-2017-000101
                10.1136/svn-2017-000101
                5829945
                29507784
                56501dfa-1291-463b-b4d4-80c759c73024
                © Article author(s) (or their employer(s) unless otherwise stated in the text of the article) 2017. All rights reserved. No commercial use is permitted unless otherwise expressly granted.

                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/

                History
                : 12 June 2017
                : 14 June 2017
                Categories
                Review
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                big data,deep learning,neural network,support vector machine,stroke

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