Journal of Artificial Intelligence for Medical Sciences: Announcements http://ojs.ais.cn/jaims <p>The <em><strong>Journal of Artificial Intelligence for Medical Sciences </strong></em>(<strong>JAIMS,</strong> <strong>Online ISSN 2666-1470</strong>) is an international peer reviewed journal that covers all aspects of theoretical, methodological and applied artificial intelligence (AI) for medical sciences, healthcare and life sciences.</p> <p>The Editors welcome original research articles, comprehensive reviews, correspondences and perspectives that provide novel insights into diagnostics, drug development, care processes, treatment personalization with the support of machine/deep learning, data science, natural language processing (NLP), etc.</p> <p>Research areas covered in the journal include, but are not limited to, the following:</p> <ul> <li>Precision medicine</li> <li>Semantic technology for medicine</li> <li>Medical knowledge graphs and ontologies</li> <li>Machine learning and deep learning for medicine</li> <li>AI in bio-informatics</li> <li>AI for mental health</li> <li>NLP for medical data processing</li> <li>Medical data mining</li> <li>Ontology/knowledge engineering for medicine</li> <li>AI for patient data processing and management</li> <li>Epidemic outbreak prediction</li> <li>(Bio-)medical knowledge acquisition and management</li> <li>Computerized clinical practice / clinical guidelines (CPGs) and protocols</li> <li>Biomedical imaging and signal processing</li> <li>Visual analytics in biomedicine</li> <li>Clinical decision support systems (CDSS)</li> <li>Drug discovery</li> <li>Case prioritization</li> <li>Chatbots in medical science</li> <li>AI in gene editing</li> </ul> <h2> </h2> <h2 id="open-access-and-publication-fees">Open Access</h2> <p>This is an <strong>open access</strong> journal, i.e. all articles are immediately and permanently free to read, download, copy &amp; distribute. The journal is published under the <a href="https://creativecommons.org/licenses/by-nc/4.0/" target="_blank" rel="noopener"><strong>CC BY-NC 4.0</strong></a> user license which defines the permitted 3rd-party reuse of its articles. Publication in this journal is <strong>free of charge</strong> for Authors. All open access publication fees are subsidized by Guangdong AiScholar Institute of Academic Exchange (GDAIAE) . Note that this is a <em>promotional offer</em> which applies to all papers submitted before <strong>31 December 2022</strong>.</p> <h2> </h2> <h2 id="indexation">Indexation</h2> <p>The <em>Journal of Artificial Intelligence for Medical Sciences</em> is currently indexed in <strong>Ulrich's Periodicals Directory</strong> (Ulrichsweb), <strong>Google Scholar</strong>, the <strong>China National Knowledge Infrastructure</strong> (CNKI) and <strong>Wanfang Data</strong>.</p> en-US Call for paper for the Special Issue: Artificial Intelligence in Adolescent Mental Health http://ojs.ais.cn/jaims/announcement/view/6 <p><strong>Aims and Scope</strong></p> <p>Adolescence is a critical period of physical and mental development. It’s also a high-risk period for the development of mental disorders. In recent years, the global mental health problems of adolescents are growing viciously. Adolescent mental health is closely related to social development. Artificial intelligence (AI) has great potential to solve many problems that cannot be properly solved by classical science. The field of mental health is one of the most important areas where the potential of AI can be exploited, such as recognition, diagnosis,treatment, nursing and rehabilitation of mental health problems. In addition, artificial intelligence can also be used to establish risk warning models to achieve the goal of personalized long-term care for mental health problems, and there are clear societal benefits to deal with adolescent psychiatric problems with the technical advantages of AI. Meanwhile, the research related to artificial intelligence and adolescent mental health is in its infancy, and more researches are still needed to discover or determine the role of AI.</p> <p> </p> <p><strong>Guest Editors</strong></p> <p><strong>Prof. Bingxiang Yang</strong></p> <p>School of Nursing, Wuhan University, Wuhan, China</p> <p><strong>Prof. Jieyao Shi</strong></p> <p>Suzhou Vocational University, Suzhou, China</p> <p><strong> Prof. Yufang Yang</strong></p> <p>School of Nurshing, Capital Medical University, Beijing, China</p> <p><strong>Prof. Zhisheng Huang</strong></p> <p>Department of Computer Science, Faculty of Sciences, Free University (VU), Amsterdam, The Netherlands</p> <p> </p> <p><strong>The potential topics of this special issue include, but are not limited to:</strong></p> <ul> <li>Adolescentmental problems risk prediction in relation to AI </li> <li>Adolescent mental problems diagnosis, treatment, nursing, rehabilitation in relation to AI</li> <li>Ethical issues of adolescent mental healthin relation to AI</li> <li>Mentalhealth data governance in relation to AI, including data privacy and data security</li> </ul> <h1> </h1> <p><strong>Important Dates</strong></p> <p>Submissionof papers: 28 February 2022<br />Notification ofreview results: 31 April 2023<br />Submission ofrevised papers: 30 May 2023<br />Notification of finalreview results: 30 June 2023</p> <h1> </h1> <p><strong>Submit Your Paper</strong></p> <p>To access the online submission site for the journal, please visit: <a href="http://oapublishing-jaims.com/jaims/about/submissions"><u>http://oapublishing-jaims.com/jaims/about/submissions</u></a>. Instructions will be provided on screen and you will be stepwise guided through the process of uploading all the relevant article details and files associated with your submission. All manuscripts must be in the English language.</p> <p>Note that if this is the first time that you submit to the <strong><em>Journal of Artificial Intelligence for Medical Sciences</em></strong>, you need to register as a user of the <a href="https://www.editorialmanager.com/joaims/default2.aspx">Editorial Manager system</a> first.</p> <p>NOTE : Before submitting your paper, please make sure to review the journal's <a href="#authorGuidelines"><u>Author Guidelines</u></a> first.</p> <h1> </h1> <p><strong>Introduction of Guest Editors</strong></p> <p><strong>Prof. Bingxiang Yang</strong></p> <p>Bing Xiang Yang RN, Ph.D., FAAN,Young Top-notch Talent Cultivation Program of Hubei Province <br />She is currently a Full Associate Professor with the School of Nursing, and a Researcher with the Department of Psychiatry, Renmin Hospital, Wuhan University, Wuhan, China. She got three national funding to support the mental health promotion researches and published at least ten international papers, some papers were published at the top medicine journals, such as The Lancet Global Health and American Journal of Psychiatry. She also worked as a Counsellor and a Professional Volunteer for the volunteer group Tree Hole Rescue Team, which utilizing AI to assist suicide monitoring and intervention among social network users. Her research interest is interdisciplinary study of mental health and artificial intelligence, especially focused on AI-based suicide monitoring, and intervention.</p> <p><strong>Prof. Jieyao Shi</strong></p> <p>Jieyao Shi, Doctor of Medicine, Professor, State Psychological consultant Grade II. Director of the Training Management Committee of the "Tree Hole Operation Rescue Team"; Deputy Secretary General and of Talent Training &amp;Career Development Branch of Jiangsu Health Management Institute; Distinguished Expert of Health Education; Member of China Association of Mental Health. She has been engaged in physical and mental health education for more than 20 years and psychological consulting for over 10 years. She has presided over and participated in more than 10 scientific research projects at all levels and has published over 30 academic papers in domestic and foreign journals.</p> <p><strong>Prof. Zhisheng Huang</strong></p> <p>Professor Zhisheng Huang is a tenured senior researcher at Computer Science Department of VU University Amsterdam, the Netherlands. He is also a full professor at School of Computer Science and Engineering, Wuhan University of Science and Technology, China. His research interests include Semantic Web technology, knowledge graphs, and ontology engineering, Artificial Intelligence for medicine, and medical informatics. Prof. Huang has published about 300 papers in journals/conferences/workshops, served as a member of programme/organising committee for over 200 international conferences/workshops. He is the editor-in-chief of Journal of Artificial Intelligence for Medical Sciences.</p> <p> </p> <p> </p> Journal of Artificial Intelligence for Medical Sciences 2022-11-21 Call for paper for the Special Issue: Artificial Intelligence in Traditional Chinese Medicine http://ojs.ais.cn/jaims/announcement/view/5 <p><strong>Aims and Scope</strong></p> <p>The theme of this special issue is "<strong>Artificial Intelligence in Traditional Chinese Medicine</strong>". This Special Issue focuses on the application of artificial intelligence in the field of traditional Chinese medicine (TCM), with the aim to solve challenges in TCM intelligence and promote the development, application and clinical trails of artificial intelligence in TCM.</p> <p> </p> <p><strong>Guest Editors</strong></p> <p><strong>Prof. Junwen Wang</strong></p> <p>Institute of Basic Theory of Traditional Chinese Medicine, Chinese Academy of Traditional Chinese Medicine, China</p> <p><strong>Dr. Biqing Chen</strong></p> <p>Research Center of Chinese Medicine / Central Laboratory, Jiangsu Province Hospital of Chinese Medicine, China</p> <p><strong>Prof. Xie Dan</strong></p> <p>Hubei University of Chinese Medicine, China</p> <p><strong>Prof. Xuekun Song</strong></p> <p>Henan University of Chinese Medicine, China</p> <p> </p> <p><strong>The potential topics of this special issue include, but are not limited to:</strong></p> <ul> <li>New mode of TCM diagnosis and treatment service under the background of Internet and artificial intelligence</li> <li>Research on the clinical experience of famous old Chinese medicine</li> <li>Development and utilization of real world TCM big data</li> <li>Knowledge graph and knowledge service in the field of traditional Chinese medicine</li> <li>Virtual simulation and inheritance of traditional Chinese Medicine</li> <li>Information technology assists TCM diagnosis and treatmen</li> <li>Research and development of information collection and processing equipment for intelligent four diagnosis of traditional Chinese medicine</li> <li>Research and development of TCM intelligent wearable health monitoring device</li> <li>Research and development of TCM intelligent treatment equipment</li> <li>Data-based intelligent platform for ancient Chinese medicine books</li> <li>Electronic prescription and intelligent remote review</li> <li>Intelligent prescription screening and drug R &amp; D</li> <li>Informatization of quality control of Chinese herbal medicines</li> <li>Informatization of research on medicinal properties and efficacy of traditional Chinese medicines</li> <li>Intelligent manufacturing technology of traditional Chinese medicine</li> </ul> <p> </p> <p><strong>Important Dates</strong></p> <ul> <li><strong>Deadline of Submission: 31 Dec. 2022</strong></li> <li>Notification of review results: 28 Feb. 2023</li> <li>Submission of revised papers: 31 March 2023</li> <li>Notification of final review results: 30 Apr. 2023</li> </ul> <p> </p> <p><strong>Submit Your Paper</strong></p> <p>To access the online submission site for the journal, please visit: <a href="http://oapublishing-jaims.com/jaims/about/submissions">http://oapublishing-jaims.com/jaims/about/submissions</a>. Instructions will be provided on screen and you will be stepwise guided through the process of uploading all the relevant article details and files associated with your submission. Please select “<em>Special Issue - Artificial Intelligence in Traditional Chinese Medicine</em>” in “<strong>Section/Category</strong>” of “<strong>General Information</strong>”. All manuscripts must be in the English language.</p> <p>Note that if this is the first time that you submit to the <em>Journal of Artificial Intelligence for Medical Sciences</em>, you need to register as a user of the <a href="https://www.editorialmanager.com/joaims/default2.aspx"> Editorial Manager</a> system first.</p> <p><strong>NOTE</strong>: Before submitting your paper, please make sure to review the journal's Author Guidelines first.</p> <p> </p> <p><strong>Introduction of Guest Editors</strong></p> <p><strong>J</strong><strong>unwen Wang</strong>, Ph.D., post doctoral, visiting scholar in the United States, professor and doctoral supervisor; Outstanding young scientific and technological talents of the Chinese Academy of traditional Chinese medicine; She is the standing member and Deputy Secretary General of the sub health committee of the world China Alliance, the review expert of the National Natural Science Foundation of China and other projects, the editorial board member of medical artificial intelligence, and the reviewer of Chinese medicine journal and other journals.</p> <p>Research direction: TCM knowledge atlas, big data and intelligent auxiliary diagnosis, integrated application and innovative R &amp; D of TCM diagnosis and treatment equipment.</p> <p><strong>Dan Xie</strong> is a professor in the college of Information Engineering, Hubei University of Chinese Medicine in Wuhan, China. She received the PhD degree from the State Key Laboratory of Software Engineering of Wuhan University in 2008. Her current research interests include medical software development, machine learning, natural language processing in electronic medical records. She was a visiting scholar at the University of Tokyo in Japan and a postdoctoral fellow in the Department of Biostatistics at the Houston Health and Medical Center of the University of Texas in the United States. She is currently a member of the IEEE and a senior member of CCF, and the associate editor of the International Journal of Artificial Intelligence and Medical Sciences. She mainly participated projects in the National Institutes of Health of the United States, the Chinese medicine modernization project of the Ministry of science and technology of China, and published more than 60 papers. She has won the second prize of scientific and technological progress in Hubei Province and the third prize of teaching achievements in Hubei Province.</p> <p><strong>Xuekun Song</strong>, associate professor. He received the PhD degree in Biomedical Engineering from Harbin Medical University, PR China, in 2017. He is currently the visiting scholar at Tsinghua University and the director of the Key Laboratory of Health Big Data and Biomedical Informatics of Henan University of Traditional Chinese Medicine. He is also CCF member and IEEE member, deputy secretary general of CMIA medical informatics theory and education professional committee. He served as the communication review expert of NSFC and the contributing editor or reviewer of some journals at home and abroad, such as IEEE/ACM Transactions on Computational Biology and Bioinformatics, Frontiers in Bioinformatics, China Digital Medicine, and ACTA CHINESE MEDICINE.</p> <p><strong>Biqing Chen</strong>, Doctor of Philosophy received at Peking University, associate research fellow at Central Laboratory, Jiangsu Provincial Hospital of Chinese Medicine, director of the Clinical Research Branch of China Information Association of Traditional Chinese Medicine. I preside over one National Natural Science Foundation of China project, published more than ten SCI papers, including eight first author or correspondence articles. My main research interest is the genetic and molecular mechanism of human cognitive behaviors, and employing multi-omics approaches such as genomics, transcriptomics, and single cell sequencing in experimental design and data analysis.</p> Journal of Artificial Intelligence for Medical Sciences 2022-09-02 Welcome to attend ISAIMS 2022 conference http://ojs.ais.cn/jaims/announcement/view/4 <p><strong>2022 3rd International Symposium on Artificial Intelligence for Medical Sciences</strong><strong> (</strong><strong>ISAIMS 2022)</strong> is the annual journal conference of <strong><em>Journal of Artificial Intelligence for Medical Sciences. </em></strong>This year, it will be held during October 13-15, 2022 in Amsterdam, Netherlands, with one parallel session in Wuhan, China. <strong>ISAIMS 2022</strong> mainly covers topics on Frontier technologies of AI, biometrics, intelligent medical robots, intelligent image recognition, intelligent diagnosis and treatment, medical artificial intelligence in the post-epidemic era, etc. It welcomes all high-quality research papers and presentations from related research fields. For more information, please visit webiste: <a href="http://www.isaims.org/"><u>http://www.isaims.org/</u></a> .</p> <p><img 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 Journal of Artificial Intelligence for Medical Sciences 2022-08-11 Call for paper for the Special Issue: Artificial Intelligence in Medical Data Governance http://ojs.ais.cn/jaims/announcement/view/2 <blockquote> <h2><strong><span lang="EN-US">Aims and Scope</span></strong></h2> </blockquote> <p>In recent years, with the explosive growth of medical data and the in-depth impact of information technology with artificial intelligence as the core on medical industry, data management and application capabilities have become one of the core capabilities of hospitals, and the level of data governance will surely become a direct manifestation of the comprehensive strength of a hospital. With the help of artificial intelligence technology, the research and practice of realizing hospital data governance is still in its infancy. Data governance provides a more standardized model for data accumulation, such as continuous collection, precipitation, and classification; medical artificial intelligence requires deep learning evolution through a large number of medical data operations, so it is inseparable from the support of big data technology and data governance.</p> <p> </p> <blockquote> <h2><strong><span lang="EN-US">Guest Editors</span></strong></h2> </blockquote> <p><strong>Siwei Yu: </strong>School of Clinical Medicine, Guizhou Medical University, China</p> <p>E-mail: manfisy@163.com</p> <p><strong>Xi Chen: </strong>Big Data Research Center, University of Electronic Science and Technology, China</p> <p>E-mail: chenxi@standata.cn</p> <p><strong>Hao Fan: </strong>School of Information Management, Wuhan University, China</p> <p>E-mail: hfan@whu.edu.cn</p> <p><strong>Long Lu: </strong>University of Cincinnati, U.S.A.</p> <p>Email: bioinfo@gmail.com</p> <p><strong>Zhisheng Huang: </strong>Vrije University Amsterdam, Netherlands</p> <p>E-mail: huangzhishengnl@gmail.com</p> <p> </p> <p><strong>The potential topics of this special issue include, but are not limited to:</strong></p> <ul> <li>Metadata management</li> <li>Master data management</li> <li>Medical terminology service</li> <li>Analysis of electronic medical records</li> <li>Medical data discovery</li> <li>Medical data quality control</li> <li>Medical knowledge graph construction</li> <li>Health data governance in relation to AI/machine learning</li> <li>Data privacy and data security</li> <li>Health application based on Metadata</li> </ul> <p> </p> <blockquote> <h2><strong><span lang="EN-US">Important Dates</span></strong></h2> </blockquote> <ul> <li><strong>Submission of papers: 1 July 202</strong><strong>2</strong></li> <li>Notification of review results: 30 August 2022</li> <li>Submission of revised papers: 15 October 2022</li> <li>Notification of final review results: 31 October 2022</li> </ul> <p> </p> <blockquote> <h2><strong><span lang="EN-US">Submit Your Paper</span></strong></h2> </blockquote> <p>To access the online submission site for the journal, please visit: <a href="http://oapublishing-jaims.com/jaims/about/submissions">http://oapublishing-jaims.com/jaims/about/submissions</a>. Instructions will be provided on screen and you will be stepwise guided through the process of uploading all the relevant article details and files associated with your submission. All manuscripts must be in the English language.</p> <p>Note that if this is the first time that you submit to the <strong><em>Journal of Artificial Intelligence for Medical Sciences</em></strong>, you need to register as a user of the<a href="https://www.editorialmanager.com/joaims/default2.aspx"> Editorial Manager</a> system first.</p> <p>NOTE : Before submitting your paper, please make sure to review the journal's <a href="http://ojs.ais.cn/jaims/about/submissions#authorGuidelines">Author Guidelines</a> first.</p> <p> </p> <blockquote> <h2><strong><span lang="EN-US">Introduction of the Guest Editors</span></strong></h2> </blockquote> <p>Dr. <strong>Siwei Yu</strong> is an associate professor of Guizhou Medical University, director of Smart Hospital Construction Office of Guizhou Provincial People's Hospital, distinguished researcher of Information Resources Research Center of Wuhan University, researcher of Big Data Research Institute of Wuhan University, distinguished ICT industry teaching expert of Huawei Management Training, co-sponsor of Open Medical and Health Alliance (OMAHA) and chairman of Document Format Working Group. He has deep attainments in hospital information system construction and management, medical knowledge management, regional health informatization construction, health e-government, health care big data, medical artificial intelligence and intelligent medical education.</p> <p><strong>Xi Chen,</strong> Ph.D. in public health, University of Montpellier, France, visiting researcher at the Big Data Center of the University of Electronic Science and Technology of China, founder and CEO of Chengdu Zhixin Electronic Technology Co., Ltd., has been committed to the field of medical data governance, especially for artificial intelligence. Research, has obtained 4 related invention patents.</p> <p><strong>Long Lu</strong> is currently a professor at the School of Information Management and the Director of the Health Big Data Research Center at Wuhan University. Dr. Lu’s laboratory focuses on research in biomedical informatics and healthcare big data analysis and application. Dr. Lu has received &gt;$6-million research funding from the US National Institutes of Health (NIH), the US National Science Foundation (NSF) and private foundations. He has led or participated in more than 10 major state-level research projects funded by national talent plans, the Key R&amp;D plan of the Ministry of Science and Technology of China, the National Science Foundation of China, and the National Social Science Fund of China. He has published &gt;80 high-quality SCI papers in prestigious journals including Science and Information Sciences (H-index 32, cited &gt;4000 times), and holds numerous domestic and foreign patents. He has been invited to serve as a grant reviewer of US NIH, US NSF, Canadian NSERC, French ANR, Polish NCN and Hong Kong ITC.</p> <p>Professor <strong>Zhisheng Huang</strong> is a tenured senior researcher at Computer Science Department of VU University Amsterdam, the Netherlands. He is also a full professor at School of Computer Science and Engineering, Wuhan University of Science and Technology, China. His research interests include Semantic Web technology, knowledge graphs, and ontology engineering, Artificial Intelligence for medicine, and medical informatics. Prof. Huang has published about 300 papers in journals/conferences/workshops, served as a member of programme/organising committee for over 200 international conferences/workshops. He is the editor-in-chief of Journal of Artificial Intelligence for Medical Sciences.</p> <p> </p> Journal of Artificial Intelligence for Medical Sciences 2022-02-08 About Previous Articles http://ojs.ais.cn/jaims/announcement/view/1 Journal of Artificial Intelligence for Medical Sciences 2021-11-17