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Artificial Intelligence Meets Medicine: An Interprofessional Perspective for Disease Prediction and Prevention

A special issue of International Journal of Environmental Research and Public Health (ISSN 1660-4601).

Deadline for manuscript submissions: closed (4 April 2023) | Viewed by 4574

Special Issue Editors

School of Economics and Management, Nanjing University of Science and Technology, Nanjing 210094, China
Interests: blockchain technology and application; swarm intelligence; human-computer interaction; data mining and business intelligence

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Guest Editor
School of Medicine, Chinese University of Hong Kong, Shenzhen 518172, China.
Interests: pediatrics; clinical epidemiology; medical big data

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Guest Editor
College of Business, The University of Alabama in Huntsville, Huntsville, AL 35899, USA
Interests: supply chain management; sustainability; product deletion
School of Medical Information & Engineering, Xuzhou Medical University, Xuzhou 221000, China
Interests: medical data privacy and protection; medical information

Special Issue Information

Dear Colleagues,

With the increasing amount of data in the biomedical field, how to manage and analyze the data effectively is critical and necessary. The analysis of biomedical information will help to find solutions to global health problems, and even solve problems such as drug design and disease diagnosis and prediction.

Artificial intelligence (AI) technology is increasingly being utilized and adapted into a tool to manage and analyze medical data in a purpose-appropriate manner. Intelligent computing technology, as a booming field, can process biomedical information based on data with an interprofessional perspective, and is able to analyze the impact on disease prediction and prevention.

However, there are still many unresolved theoretical and technical issues in this area that need to be further studied. For example, how can we solve problems in existing systems by using AI? How can we integrate AI tools to assist drug design? How can we use AI for disease prediction and prevention? Are there any ethical issues when deploying AI? As the field evolves, these issues need to be critically examined.

This Special Issue aims to provide an opportunity to exchange ideas and disseminate knowledge within the medical and academic communities. We welcome a variety of technical papers related to bioinformatics, in addition to original research papers and high-quality investigation papers, as well as papers on tools, systems or applications. All papers will be initially reviewed by peer reviewers. 

Topics of interest include, but are not limited to:

  • Big data analytics methods for biological and medical data,
  • Applications using AI and big data analytics based on wearable devices,
  • AI methods for biological and medical data,
  • AI in COVID-19,
  • Analytical tools for biology and medicine,
  • Deep learning with MRI, CT, ultrasound and x-ray images,
  • Deep learning with multi-semantic image recognition,
  • Computer-aided drug design,
  • Analysis of biological sequences, structures, and networks,
  • AI and aging care system design,
  • AI medical robots,
  • AI in proactive health and ageing,
  • Digital health services for the elderly and large-scale application,
  • Chinese medicine knowledge discovery and intelligent decision support,
  • Integration and application of Chinese medicine information technology,
  • AI in rare disease screening and diagnosis in children.

Dr. Peng Zhu
Dr. Guangjun Yu
Dr. Qingyun Zhu
Dr. Xiang Wu
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. International Journal of Environmental Research and Public Health is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2500 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • big data
  • medical data
  • artificial intelligence (AI)
  • COVID-19
  • deep learning
  • digital health
  • biological data
  • MRI
  • CT
  • ultrasound
  • x-ray images

Published Papers (2 papers)

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Research

20 pages, 3705 KiB  
Article
FDI and Wellbeing: A Key Node Analysis for Psychological Health in Response to COVID-19 Using Artificial Intelligence
by Da Huo, Jingtao Yi, Xiaotao Zhang, Shuang Meng, Yongchuan Chen, Rihui Ouyang and Ken Hung
Int. J. Environ. Res. Public Health 2023, 20(6), 5164; https://doi.org/10.3390/ijerph20065164 - 15 Mar 2023
Viewed by 2177
Abstract
Developing countries are primary destinations for FDI from emerging economies following the World Investment Report 2022, including destinations in OECD countries. Based on three theoretical lenses and case analyses, we argue that Chinese outward FDI has impacts on wellbeing in destination countries, and [...] Read more.
Developing countries are primary destinations for FDI from emerging economies following the World Investment Report 2022, including destinations in OECD countries. Based on three theoretical lenses and case analyses, we argue that Chinese outward FDI has impacts on wellbeing in destination countries, and that this is an important issue for psychological health in response to COVID-19. Based on the super-efficiency DEA approach, our study investigated the impact of Chinese outward FDI on wellbeing in OECD countries. We also applied a Tabu search to identify country groups based on the relationship between Chinese outward FDI and wellbeing and we developed a key node analysis of the country groups using an immune algorithm. This research has implications for public administrators in global governance and could help shape FDI policies to improve psychological health of the destination countries in response to COVID-19. Full article
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16 pages, 2618 KiB  
Article
The Two-Stage Ensemble Learning Model Based on Aggregated Facial Features in Screening for Fetal Genetic Diseases
by Jiajie Tang, Jin Han, Bingbing Xie, Jiaxin Xue, Hang Zhou, Yuxuan Jiang, Lianting Hu, Caiyuan Chen, Kanghui Zhang, Fanfan Zhu and Long Lu
Int. J. Environ. Res. Public Health 2023, 20(3), 2377; https://doi.org/10.3390/ijerph20032377 - 29 Jan 2023
Cited by 4 | Viewed by 1760
Abstract
With the advancement of medicine, more and more researchers have turned their attention to the study of fetal genetic diseases in recent years. However, it is still a challenge to detect genetic diseases in the fetus, especially in an area lacking access to [...] Read more.
With the advancement of medicine, more and more researchers have turned their attention to the study of fetal genetic diseases in recent years. However, it is still a challenge to detect genetic diseases in the fetus, especially in an area lacking access to healthcare. The existing research primarily focuses on using teenagers’ or adults’ face information to screen for genetic diseases, but there are no relevant directions on disease detection using fetal facial information. To fill the vacancy, we designed a two-stage ensemble learning model based on sonography, Fgds-EL, to identify genetic diseases with 932 images. Concretely speaking, we use aggregated information of facial regions to detect anomalies, such as the jaw, frontal bone, and nasal bone areas. Our experiments show that our model yields a sensitivity of 0.92 and a specificity of 0.97 in the test set, on par with the senior sonographer, and outperforming other popular deep learning algorithms. Moreover, our model has the potential to be an effective noninvasive screening tool for the early screening of genetic diseases in the fetus. Full article
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