Overview
Explainability techniques provide insights into AI model decisions and highlight contributing image regions. They increase clinician trust and support clinical reasoning. Transparent explanations aid regulatory review and adoption.
Techniques
Saliency maps attention mechanisms and concept based explanations are common methods. Quantitative and qualitative evaluation of explanations is necessary. Explanations should be clinically meaningful and not misleading.
Clinical Use
Explainability helps clinicians assess AI suggestions and identify potential errors. It supports education and collaborative decision making. Clear visualization tools integrate with reporting systems.
Limitations
Explanations may oversimplify complex model behavior and create false confidence. Rigorous evaluation ensures explanations align with clinical reasoning. Combining multiple explanation methods improves robustness.