Special issue: AI-empowered research in medical imaging and bioinformatics

Introduction:

In recent years, artificial intelligence (AI) has made significant progress in the fields of medical imaging and bioinformatics. Specifically, AI technology has helped doctors to more accurately analyze and interpret a wide range of imaging data. Additionally, it has impacted areas such as genomics, proteomics, and metabolomics. These advances are largely due to the application of advanced machine learning and deep learning algorithms. AI is set to play a greater role in bringing forth benefits for human health.

This topic aims to explore the latest research and applications of AI in medical imaging and bioinformatics. By combining advanced machine learning and deep learning algorithms, AI can improve the diagnosis and analysis of medical images and accelerate the early detection and treatment of diseases. Meanwhile, the application of AI in bioinformatics allows an in-depth understanding of disease mechanisms and promotes the development of individualized medicine. Nonetheless, combining AI technology and medical expertise provides both new opportunities and challenges for medical diagnosis and treatment.

We look forward to high-quality submissions from researchers worldwide to promote the application of AI in medical imaging and bioinformatics and to advance the field further. We are committed to ensuring a swift review process with acceptance decisions to be made promptly.

Topics covered:

This special issue invites original research articles, reviews, and technical notes covering a wide range of topics related to the use of AI technologies in medical imaging and bioinformatics. Topics of interest include but are not limited to

• Development of new AI algorithms and models for automated segmentation, feature extraction, and abnormality detection in medical images.
• Integrating multimodal medical imaging data through AI improves the diagnosis and treatment of complex diseases.
• AI techniques such as Generative Adversarial Networks (GAN) are used to generate realistic synthetic medical image data for data enhancement and model training.
• Using AI and bioinformatics data to study individualized medicine and precision treatment strategies.
• Developing AI-based models to predict drug response, disease progression, and treatment efficacy.
• Combining AI, bioinformatics data, and clinical data to study disease typing and prognosis to support disease management and treatment decisions.
• Development of new AI algorithms and models for medical image reconstruction and enhancement to improve image quality and visibility of detail.
• Explore how AI technology can be used to improve aspects of noise removal, artifact removal, and resolution enhancement in medical images.
• Develop privacy-preserving AI algorithms and techniques to protect the privacy and security of medical imaging and bioinformatics data against data leakage and misuse.

Important Deadlines:

• Manuscript submission deadline: March 30, 2024
• The first round of reviews: April 30, 2024
• Revised manuscript submission: May 30, 2024
• Final acceptance notification: June 30, 2024

Submission Instructions: Please read the [Guide for Authors – https://www.keaipublishing.com/en/journals/meta-radiology] before submitting.

All articles should be submitted via the link: https://www2.cloud.editorialmanager.com/metrad/default2.aspx

Contact Information: For any inquiries related to this special issue, please contact the guest editors at bixiaan@hnu.edu.cn

Guest Editors:

• Prof. Xia-an Bi, Hunan Normal University, Changsha, China. Email: bixiaan@hnu.edu.cn
• Prof. Xiang Li, Massachusetts General Hospital and Harvard Medical School, Boston, MA, US.A. Email: xli60@mgh.harvard.edu
• Prof. Sheng He, Boston Children's Hospital/Harvard Medical School, Boston, MA, USA. Email: heshengxgd@gmail.com
• Prof. Tianming Liu, University of Georgia, Athens, GA, USA. Email: tliu@cs.uga.edu