Explainable AI in Medical Imaging: Toward Transparent and Trustworthy Diagnostic Systems
A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Biomedical Engineering".
Deadline for manuscript submissions: 20 November 2025
Special Issue Editors
Interests: digital sound processing and analyis; EEG/MEG brain signal analysis
Special Issues, Collections and Topics in MDPI journals
Interests: medical imaging; deep learning; breast cancer diagnosis; robotics
Special Issues, Collections and Topics in MDPI journals
Interests: medical image processing; breast cancer detection; pattern recognition
Special Issues, Collections and Topics in MDPI journals
Interests: medical imaging; pattern recognition
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Artificial intelligence (AI) can be readily integrated into medical imaging to vastly improve diagnostics; however, the black box nature of many AI models, particularly deep learning systems, hinders clinical adoption. The field of explainable AI (XAI) represents an important area of both research and application that offers routes toward transparency, interpretability, and trust in automated decision-making systems.
This Special Issue will cover new methods, frameworks, and applications that improve the interpretability of AI systems in medical imaging. Contributions that focus on theoretical foundations, algorithmic developments, evaluation strategies, and clinical validations of XAI techniques are welcomed. We are particularly interested in submissions which achieve a compromise of high performance along with important insights for clinicians, radiologists, and medical professionals.
Dr. Athanasios Koutras
Dr. Dermatas Evangelos
Dr. Ioanna Christoyianni
Dr. George Apostolopoulos
Guest Editors
Manuscript Submission Information
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Keywords
- XAI
- medical imaging
- interpretable AI
- deep learning
- machine learning
- clinical decision support
- model transparency
- healthcare AI
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