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      Global Evolution of Research in Artificial Intelligence in Health and Medicine: A Bibliometric Study


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          The increasing application of Artificial Intelligence (AI) in health and medicine has attracted a great deal of research interest in recent decades. This study aims to provide a global and historical picture of research concerning AI in health and medicine. A total of 27,451 papers that were published between 1977 and 2018 (84.6% were dated 2008–2018) were retrieved from the Web of Science platform. The descriptive analysis examined the publication volume, and authors and countries collaboration. A global network of authors’ keywords and content analysis of related scientific literature highlighted major techniques, including Robotic, Machine learning, Artificial neural network, Artificial intelligence, Natural language process, and their most frequent applications in Clinical Prediction and Treatment. The number of cancer-related publications was the highest, followed by Heart Diseases and Stroke, Vision impairment, Alzheimer’s, and Depression. Moreover, the shortage in the research of AI application to some high burden diseases suggests future directions in AI research. This study offers a first and comprehensive picture of the global efforts directed towards this increasingly important and prolific field of research and suggests the development of global and national protocols and regulations on the justification and adaptation of medical AI products.

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          Artificial intelligence in medicine.

          Artificial Intelligence (AI) is a general term that implies the use of a computer to model intelligent behavior with minimal human intervention. AI is generally accepted as having started with the invention of robots. The term derives from the Czech word robota, meaning biosynthetic machines used as forced labor. In this field, Leonardo Da Vinci's lasting heritage is today's burgeoning use of robotic-assisted surgery, named after him, for complex urologic and gynecologic procedures. Da Vinci's sketchbooks of robots helped set the stage for this innovation. AI, described as the science and engineering of making intelligent machines, was officially born in 1956. The term is applicable to a broad range of items in medicine such as robotics, medical diagnosis, medical statistics, and human biology-up to and including today's "omics". AI in medicine, which is the focus of this review, has two main branches: virtual and physical. The virtual branch includes informatics approaches from deep learning information management to control of health management systems, including electronic health records, and active guidance of physicians in their treatment decisions. The physical branch is best represented by robots used to assist the elderly patient or the attending surgeon. Also embodied in this branch are targeted nanorobots, a unique new drug delivery system. The societal and ethical complexities of these applications require further reflection, proof of their medical utility, economic value, and development of interdisciplinary strategies for their wider application.
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            GAN-based Synthetic Medical Image Augmentation for increased CNN Performance in Liver Lesion Classification

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              Artificial intelligence as a medical device in radiology: ethical and regulatory issues in Europe and the United States

              Abstract Worldwide interest in artificial intelligence (AI) applications is growing rapidly. In medicine, devices based on machine/deep learning have proliferated, especially for image analysis, presaging new significant challenges for the utility of AI in healthcare. This inevitably raises numerous legal and ethical questions. In this paper we analyse the state of AI regulation in the context of medical device development, and strategies to make AI applications safe and useful in the future. We analyse the legal framework regulating medical devices and data protection in Europe and in the United States, assessing developments that are currently taking place. The European Union (EU) is reforming these fields with new legislation (General Data Protection Regulation [GDPR], Cybersecurity Directive, Medical Devices Regulation, In Vitro Diagnostic Medical Device Regulation). This reform is gradual, but it has now made its first impact, with the GDPR and the Cybersecurity Directive having taken effect in May, 2018. As regards the United States (U.S.), the regulatory scene is predominantly controlled by the Food and Drug Administration. This paper considers issues of accountability, both legal and ethical. The processes of medical device decision-making are largely unpredictable, therefore holding the creators accountable for it clearly raises concerns. There is a lot that can be done in order to regulate AI applications. If this is done properly and timely, the potentiality of AI based technology, in radiology as well as in other fields, will be invaluable. Teaching Points • AI applications are medical devices supporting detection/diagnosis, work-flow, cost-effectiveness. • Regulations for safety, privacy protection, and ethical use of sensitive information are needed. • EU and U.S. have different approaches for approving and regulating new medical devices. • EU laws consider cyberattacks, incidents (notification and minimisation), and service continuity. • U.S. laws ask for opt-in data processing and use as well as for clear consumer consent.

                Author and article information

                J Clin Med
                J Clin Med
                Journal of Clinical Medicine
                14 March 2019
                March 2019
                : 8
                : 3
                [1 ]Institute for Preventive Medicine and Public Health, Hanoi Medical University, Hanoi 100000, Vietnam
                [2 ]Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD 21205, USA; carl.latkin@ 123456jhu.edu
                [3 ]Center of Excellence in Artificial Intelligence in Medicine, Nguyen Tat Thanh University, Ho Chi Minh City 700000, Vietnam; giang.coentt@ 123456gmail.com (G.T.V.); ngaiman_cheung@ 123456sutd.edu.sg (N.-M.C.)
                [4 ]Center of Excellence in Evidence-based Medicine, Nguyen Tat Thanh University, Ho Chi Minh City 700000, Vietnam; nurtwsw@ 123456nus.edu.sg
                [5 ]Institute for Global Health Innovations, Duy Tan University, Da Nang 550000, Vietnam; giang.ighi@ 123456gmail.com (G.H.H.); huong.ighi@ 123456gmail.com (H.L.T.N.)
                [6 ]Center for Interdisciplinary Social Research, Phenikaa University, Yen Nghia, Ha Dong District, Hanoi 100803, Vietnam; hoang.vuongquan@ 123456phenikaa-uni.edu.vn (Q.-H.V.); tung.homanh@ 123456phenikaa-uni.edu.vn (M.-T.H.); lvphuong@ 123456gmail.com (V.-P.L.); toan.homanh@ 123456phenikaa-uni.edu.vn (M.-T.H.)
                [7 ]Faculty of Economics and Finance, Phenikaa University, Yen Nghia, Ha Dong district, Hanoi 100803, Vietnam
                [8 ]Sciences Po Paris, Campus de Dijon, 21000 Dijon, France; thutrang.vuong@ 123456sciencespo.fr
                [9 ]Vietnam-Germany Hospital, 16 Phu Doan street, Hoan Kiem district, Hanoi 100000, Vietnam; kimcuongvd@ 123456gmail.com
                [10 ]Alice Lee Centre for Nursing Studies, Yong Loo Lin School of Medicine, National University of Singapore, Singapore 119228, Singapore
                [11 ]Information Systems Technology and Design (ISTD) pillar, Singapore University of Technology and Design, Singapore 487372, Singapore
                [12 ]A.I. for Social Data Lab (AISDL), Vuong & Associates, 3/161 Thinh Quang, Dong Da District, Hanoi 100000, Vietnam; htn2107@ 123456caa.columbia.edu
                [13 ]Department of Psychological Medicine, National University Hospital, Singapore 119228, Singapore; cyrushosh@ 123456gmail.com
                [14 ]Center of Excellence in Behavioral Medicine, Nguyen Tat Thanh University, Ho Chi Minh City 700000, Vietnam; pcmrhcm@ 123456nus.edu.sg
                [15 ]Department of Psychological Medicine, Yong Loo Lin School of Medicine, National University of Singapore, Singapore 119228, Singapore
                [16 ]Biomedical Global Institute of Healthcare Research & Technology (BIGHEART), National University of Singapore, Singapore 117599, Singapore
                Author notes
                [* ]Correspondence: bach.ipmph@ 123456gmail.com ; Tel.: +84-98-222-8662
                © 2019 by the authors.

                Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ( http://creativecommons.org/licenses/by/4.0/).


                bibliometric analysis,artificial intelligence,health,medicine,global,mapping,ai ethics


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