Explainable Artificial Intelligence in Biomedical Image Analysis: A Comprehensive Survey
Journal:
arXiv
Published Date:
Jul 9, 2025
Abstract
Explainable artificial intelligence (XAI) has become increasingly important
in biomedical image analysis to promote transparency, trust, and clinical
adoption of DL models. While several surveys have reviewed XAI techniques, they
often lack a modality-aware perspective, overlook recent advances in multimodal
and vision-language paradigms, and provide limited practical guidance. This
survey addresses this gap through a comprehensive and structured synthesis of
XAI methods tailored to biomedical image analysis.We systematically categorize
XAI methods, analyzing their underlying principles, strengths, and limitations
within biomedical contexts. A modality-centered taxonomy is proposed to align
XAI methods with specific imaging types, highlighting the distinct
interpretability challenges across modalities. We further examine the emerging
role of multimodal learning and vision-language models in explainable
biomedical AI, a topic largely underexplored in previous work. Our
contributions also include a summary of widely used evaluation metrics and
open-source frameworks, along with a critical discussion of persistent
challenges and future directions. This survey offers a timely and in-depth
foundation for advancing interpretable DL in biomedical image analysis.
