Explainable Deep Learning Methods in Medical Image Classification: A Survey

Accuracy alone is insufficient for clinical adoption: Despite deep learning models achieving human-level accuracy in medical image classification, they remain largely unused in actual clinical workflows primarily due to lack of interpretability—demonstrating that performance metrics must be paired with explainability for real-world medical applications.

Multiple explanation approaches are needed: The survey identifies four distinct categories of explanation methods (visual, textual, example-based, and concept-based), suggesting that no single approach adequately addresses all clinical needs and that responsible AI in medicine requires diverse interpretability strategies.

Explanation quality requires standardized evaluation: The paper reviews existing metrics for evaluating explanation quality, highlighting that the field recognizes the need for systematic assessment methods—a critical step toward ensuring explanations are actually trustworthy and useful rather than merely appearing transparent.

The "black box" problem directly impacts patient safety and trust: The survey emphasizes that interpretability barriers are a primary obstacle to clinical adoption, indicating that responsible AI deployment in healthcare requires solving explainability challenges, not just achieving technical accuracy.

Standardized datasets and benchmarks are still developing: The review of existing medical imaging datasets and evaluation metrics suggests the field is still establishing foundational infrastructure for responsible AI, meaning best practices for explainable medical AI are still being defined.