Explainable AI in Medical Imaging: Toward Transparent and Trustworthy Diagnostic Systems

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

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