Unveiling the Decision Veil: Explainable AI in Medical Imaging

The realm of medical imaging is undergoing a transformative shift with the rapid integration of Artificial Intelligence (AI). This evolution necessitates clear decision-making protocols to foster trust and ensure successful clinical adoption. A critical component of this transition is Explainable AI (XAI), which focuses on making AI systems more transparent, interpretable, and reliable for diagnostic and prognostic applications. Recent strides in XAI are poised to significantly impact how medical imaging systems process and deliver data. Yet, challenges remain, including ethical considerations, interpretability across diverse imaging modalities, and the alignment of XAI systems with clinical workflows and regulations.

This Research Topic aims to explore the latest XAI methodologies for medical imaging, addressing the pressing need for clarity and reliability in AI-deployed systems. We welcome articles addressing, but not limited to, the following themes:

* Interpretability methods for medical image classification and segmentation
* Explainability approaches in multimodal imaging and data fusion
* Saliency maps and attention-based mechanisms for medical imaging diagnostics
* Ethical considerations and trustworthiness of XAI systems in clinical practice
* Frameworks for human-AI interaction and collaboration in clinical workflows
* Legal and regulatory compliance implications of deploying XAI in healthcare
* Robustness and uncertainty quantification in explainable medical imaging AI
* Advanced visualization techniques for improved clinician understanding of AI decisions
* Explainable Deep Reinforcement Learning for sequential medical imaging tasks
* Graph Neural Networks for explainable relational modeling in medical imaging
* Interpretability techniques tailored to quantum-enhanced medical imaging applications
* Generative AI methods and their explainability in medical imaging
* Bias detection, fairness, and transparency in explainable medical imaging AI
* Clinical validation and evaluation metrics for Explainable AI methods in medical diagnostics

Article types and fees

This Research Topic accepts the following article types, unless otherwise specified in the Research Topic description:

• Brief Research Report
• Case Report
• Data Report
• Editorial
• FAIR² Data
• FAIR² DATA Direct Submission
• Hypothesis and Theory
• Methods
• Mini Review
• ... View all formats

Articles that are accepted for publication by our external editors following rigorous peer review incur a publishing fee charged to Authors, institutions, or funders.

Article types

This Research Topic accepts the following article types, unless otherwise specified in the Research Topic description:

• Brief Research Report
• Case Report
• Data Report
• Editorial
• FAIR² Data
• FAIR² DATA Direct Submission
• Hypothesis and Theory
• Methods
• Mini Review
• Original Research
• Perspective
• Review
• Technology and Code

Keywords: explainable AI, medical imaging, XAI

Important note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.