Achievements and Challenges in Explaining Deep Learning based Computer-Aided Diagnosis Systems

Explainability is legally mandated, not optional: The paper emphasizes that stricter legislation worldwide now requires AI systems to provide "clear and justifiable explanations" for their decisions, making transparency a regulatory requirement rather than just an ethical preference in medical AI applications.

Multiple explanation methods exist but each has limitations: The paper analyzes various approaches including visual explanations (like heatmaps), textual explanations, and both "post-hoc" (explaining decisions after they're made) and "ante-hoc" (building explainability into the model from the start) methods, suggesting no single approach fully solves the explainability challenge.

Medical AI requires exceptional transparency standards: In healthcare contexts specifically, the paper stresses that explainability is "of utmost importance" because AI diagnoses directly affect patient treatment and well-being, making the stakes higher than in many other AI applications and requiring a careful transition from research to clinical practice.

Explainable AI can validate and improve medical knowledge: Beyond just transparency, the paper highlights that explanation methods have achieved success in validating known disease criteria, discovering new potential biomarkers, and enabling correction of AI models—meaning explainability serves both safety and scientific advancement.

Significant practical challenges remain unresolved: Despite progress, the paper identifies "remaining challenges" that prevent AI from being safely deployed as a clinical decision support tool, indicating that current explainability methods are not yet sufficient for real-world medical practice.