Explainable AI And Visual Reasoning: Insights From Radiology

Heat maps alone don't build trust: Traditional visual explanation methods like heat maps fail to gain radiologist trust despite showing where AI systems focus, because they don't provide the complete evidentiary reasoning that practitioners need to validate a diagnosis.

AI explanations should mirror human reasoning: The paper demonstrates that radiologists justify diagnoses through a structured process of evidence presentation and reasoning. XAI systems that replicate this human approach to building a case—rather than simply highlighting image regions—are more likely to be adopted and trusted by medical professionals.

Current XAI lacks "evidentiary grounding": Machine-learned classifications fail to explain the full chain of reasoning that connects visual evidence to conclusions, creating a critical gap between what AI systems can show and what practitioners need to see to feel confident in the results.

Transparency alone is insufficient for adoption: The paper reveals that explainability features don't automatically lead to user adoption; explanations must be designed around practitioners' actual workflows and how they convince colleagues of diagnostic validity, not just technical justification of predictions.

Principles from radiology could improve XAI broadly: The insights about human-centered explanation design based on evidence-based reasoning may apply beyond radiology to other high-stakes fields where AI adoption depends on professional trust and understanding.