Artificial Intelligence and Machine Learning for Medical Image Processing
A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Bioelectronics".
Deadline for manuscript submissions: 15 February 2026 | Viewed by 14
Special Issue Editor
Interests: medical image analysis; machine learning; computer aided skin diagnostics; treatment evaluation
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Artificial intelligence (AI) and machine learning (ML) have become transformative tools in medical image processing, offering innovative solutions to longstanding challenges in clinical diagnostics, treatment planning, and disease monitoring. In response to the exponential growth of medical imaging data and the increasing demand for precision medicine, this Special Issue will bring together cutting-edge research that leverages AI and ML techniques to enhance the interpretation, analysis, and utility of medical images across a wide range of applications.
This Special Issue welcomes original research and comprehensive review articles that explore the integration of AI methodologies with medical imaging. We encourage submissions addressing a wide range of tasks, including segmentation, classification, detection, synthesis, and registration. All types of medical imaging modalities are welcome, including but not limited to MRI, CT, ultrasound, PET, dermatoscopy, endoscopy, and X-ray imaging.
We particularly encourage submissions that propose novel machine learning models, efficient training strategies, interpretable AI solutions, and/or robust validation pipelines. Papers addressing real-world clinical applications, data-driven discovery, and system-level integration of AI into medical workflows are also welcome.
We invite researchers to contribute their most recent findings to this Special Issue, helping to shape the future of AI-driven medical image processing.
Dr. Panagiota Spyridonos
Guest Editor
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Keywords
- artificial intelligence
- machine learning
- medical image processing
- deep learning
- medical image segmentation
- classification
- detection
- transformer networks
- convolutional neural networks (CNNs)
- medical imaging modalities (MRI, CT, X-ray, Ultrasound, PET)
- computer-aided diagnosis (CAD)
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