Generalizable and Explainable Deep Learning for Medical Image Computing: An Overview

Different explainability methods have varying effectiveness: The research found that some explanation techniques like XgradCAM successfully identify relevant abnormal regions in medical images, while others like EigenGradCAM performed less effectively. This suggests that choosing the right explanation method matters—not all approaches work equally well for helping clinicians understand AI decisions.

Explainability can increase clinician confidence in AI predictions: The study measured "confidence increase" as a metric and found XgradCAM produced higher confidence gains (0.12 in glioma tumors) compared to other methods like GradCAM++ (0.09). This indicates that transparent AI explanations don't just satisfy curiosity—they can meaningfully improve how much doctors trust AI recommendations.

Current AI models show promising but imperfect accuracy in medical imaging: ResNet50 achieved 86.31% accuracy on skin cancer classification across multiple datasets. While this demonstrates feasibility for clinical use, the 13.69% error rate highlights why explainability is critical—clinicians need to understand not just what the AI recommends, but why, especially when errors could have serious consequences.

Transparency and explainability are essential for clinical adoption: The paper emphasizes that addressing "the urgent need for transparency and explainability" is necessary for medical AI to be used responsibly in real clinical settings, suggesting that accuracy alone is insufficient without doctors being able to understand and verify the AI's reasoning.