Medical Imaging Analysis: Current and Future Trends

Dear Colleagues,

Medical image analysis is a rapidly advancing field that leverages deep learning and machine learning to enhance computer-aided diagnostic accuracy and support clinicians in treatment planning. From traditional image processing techniques to cutting-edge deep learning models and foundation models, the field has achieved significant progress. This Special Issue aims to explore the latest techniques and future trends in medical image analysis, with a focus on innovations that bridge the gap between technological advancements and clinical practice.

Over the past decade, deep learning-based algorithms have driven the development of state-of-the-art methods for medical image analysis, including applications in disease prediction and anatomical segmentation. With the advent of large-scale and foundation models, multi-modality learning has enabled researchers to leverage diverse data sources, further enhancing diagnostic capabilities. Current research trends are shifting towards disease progression prediction and monitoring, offering critical insights into disease development over time. Additionally, there is increasing emphasis on the interpretability of AI models, which is crucial for their adoption in clinical workflows. Key challenges remain in integrating medical image analysis algorithms into real-world applications, particularly in designing effective human–computer interaction systems that support clinicians in decision-making.

This Special Issue on “Medical Image Analysis: Current and Future Trends” aims to provide a venue for presenting recent findings in the field of medical image analysis. Researchers, healthcare professionals, and AI practitioners are encouraged to submit papers with the applications to the medical image analysis for disease detection, diagnosis, and prognosis. The topics of interest for this Special Issue include, but are not limited to, the following:

1. Deep learning and foundation models for medical image analysis;
2. Interpretable and trustworthy AI for medical imaging;
3. Multi-modality data learning;
4. Disease progression prediction and monitoring;
5. Computer-aided diagnosis systems;
6. Human-computer interaction for healthcare.

Dr. He Zhao
Dr. Lin Gu
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. Bioengineering 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 2700 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.

• computer vision
• artificial intelligence (AI)
• multi-modality learning
• disease progression monitoring
• explainable AI
• human-computer interaction