Explainable AI For COVID-19 CT Classifiers: An Initial Comparison Study

Explainable AI is essential for clinical adoption: The paper emphasizes that deep learning models used in medical imaging operate as "black boxes," making it difficult for clinicians, regulators, and patients to understand how AI reaches diagnostic decisions. XAI methods help unlock this mystery by showing which features and patterns the AI actually uses to classify COVID-19 CT scans.

Multiple stakeholders need AI transparency: The study identifies that various groups—including domain experts (radiologists), regulatory agencies, managers, and patients affected by AI decisions—all require clear explanations of how AI systems work. This highlights that responsible AI isn't just a technical concern but a requirement for building trust across healthcare organizations.

Visual explanations can enhance clinical decision-making: The paper demonstrates that XAI produces both "quantification and qualitative visualisations" that can help clinicians understand which regions of CT scans the AI identified as significant for COVID-19 classification, potentially improving how doctors use these tools in practice.

AI transparency is critical for high-stakes medical applications: By focusing on COVID-19 CT classification, the study underscores that when AI systems influence patient diagnosis and treatment decisions, explainability becomes non-negotiable rather than optional—clinicians need to verify that the AI is looking at medically relevant features.