Explainable Artificial Intelligence (XAI) in Medical Imaging

Dear Colleagues,

Using artificial intelligence (AI) in medical imaging can help transform diagnostics, prognosis, and clinical decision-making. However, the widespread adoption of AI in clinical settings is commonly limited by a lack of transparency and interpretability. Explainable Artificial Intelligence (XAI) addresses this critical need by providing insights into how AI systems arrive at specific predictions, hoping to increase clinician trust and facilitate regulatory compliance.

Current XAI techniques—such as SHapley Additive exPlanations (SHAP), Local Interpretable Model-agnostic Explanations (LIME), and Gradient-weighted Class Activation Mapping (Grad-CAM)—have demonstrated utility across various imaging modalities. Yet, significant knowledge gaps remain. These include the lack of standardized benchmarks for evaluating explanation quality, insufficient integration of clinical context in interpretability frameworks, and limited understanding of how explanations influence clinical decision-making.

This Special Issue welcomes original research, reviews, and perspective articles on novel XAI methods as applied to medical imaging. Within this context, topics of interest for this Special Issue include, but are not limited to, the following:

• Applications of XAI to emerging clinical problems
• Critical analyses of current XAI pitfalls and limitations
• Development and validation of new XAI techniques
• Address ethical and regulatory standards surrounding XAI clinical use

Successful submissions will provide rigorous technical validation, incorporate clinical relevance, and offer transparent methodological reporting. We encourage interdisciplinary collaborations between computer scientists and clinicians to ensure both algorithmic robustness and real-world applicability.

Authors are advised to clearly articulate the interpretability goals of their work and to align their submissions with the broader mission of safe, equitable, and trustworthy AI in medicine.

Dr. Quincy A. Hathaway
Dr. Yashbir Singh
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.

• Explainable Artificial Intelligence (XAI)
• medical imaging
• SHAP/LIME/Grad-CAM