MiMICRI: Towards Domain-centered Counterfactual Explanations of Cardiovascular Image Classification Models

Domain expertise must be central to AI explanations in medicine: The paper demonstrates that explainability methods developed without input from medical experts often fail to align with how clinicians actually think about and validate AI models. MiMICRI's domain-centered approach—allowing doctors to interactively examine specific anatomical structures—proved more useful for expert reasoning than generic explanation methods.

Current AI explanations for medical imaging lack clinical validation: Many existing explainable AI methods don't contextualize their explanations in terms of medical knowledge or verify that model reasoning matches established clinical facts. This gap between technical explanations and medical reality represents a significant barrier to trustworthy AI deployment in healthcare.

Counterfactual explanations show promise but face plausibility challenges: While the framework helped experts understand which image features influenced model predictions, the evaluation revealed concerns about whether the generated counterfactual images were clinically realistic. This highlights that even well-designed explanation tools must be carefully validated to ensure they don't mislead clinicians.

Interactive tools empower experts to validate AI against medical knowledge: By allowing doctors to modify specific anatomical segments and observe how predictions change, MiMICRI enabled experts to test whether models aligned with known medical principles—a critical step for building appropriate trust in AI systems before clinical use.

Responsible AI requires collaboration between technologists and domain specialists: The paper's core finding is that developing trustworthy medical AI explanations demands ongoing partnership with clinicians, not just technical innovation alone.