Explainable Artificial Intelligence in Biomedical Image Analysis: A Comprehensive Survey

Explainability is critical for clinical adoption: The survey emphasizes that transparency and interpretability of AI models are essential for doctors and healthcare systems to trust and actually use deep learning tools in real medical settings, not just in research labs.

Different medical imaging types need different explanation approaches: The paper introduces a "modality-centered taxonomy," meaning that explaining AI decisions in X-rays requires different methods than explaining decisions in MRI or ultrasound images—a distinction previous surveys overlooked but that matters for practical implementation.

Multimodal AI and vision-language models are reshaping explainability: The survey highlights that newer AI systems combining multiple types of data (images plus text, for example) present new interpretability challenges and opportunities that weren't adequately addressed in earlier research, suggesting this is an emerging frontier in responsible biomedical AI.

Standardized evaluation metrics and open tools are now available: The paper catalogs widely-used frameworks and metrics for assessing whether explanations are actually good, providing practitioners with concrete resources to evaluate and compare different explainability methods rather than relying on ad-hoc approaches.

Significant challenges remain in making AI explanations truly useful: Despite progress, the survey identifies persistent gaps between technical explainability and what clinicians actually need to understand and trust AI recommendations in high-stakes medical decisions.