Digital Histopathology with Graph Neural Networks: Concepts and Explanations for Clinicians

Explainability is achievable in medical AI: The paper demonstrates that deep learning models used in cancer diagnosis can be made interpretable through techniques like GCExplainer and Logic Explained Networks, directly addressing the "black-box" problem that has hindered clinical adoption of AI tools.

Concept-based explanations help clinicians understand AI decisions: By automatically discovering and explaining the visual concepts (such as specific tissue patterns) that the AI uses to make predictions, the approach enables clinicians to verify whether the model is reasoning based on medically relevant features rather than spurious correlations.

Graph-based AI may be particularly suitable for medical imaging: The use of Graph Neural Networks to analyze histopathology slides suggests that representing tissue structures as interconnected elements could be a more interpretable way to model the spatial relationships clinicians naturally observe in pathology.

Trustworthiness requires transparency at multiple levels: The combination of automated concept discovery with logical explanations indicates that building clinician trust in AI requires both identifying what the model learned and why it made specific predictions on individual cases.

Practical implementation is demonstrated on real clinical data: Testing on H&E (hematoxylin and eosin) stained breast cancer slides shows this isn't purely theoretical—the approach works with actual pathology data, suggesting potential for real-world clinical deployment.