CrossEAI: Using Explainable AI to generate better bounding boxes for Chest X-ray images

Explainability serves a legal and ethical purpose in healthcare AI: The paper emphasizes that medical AI systems are legally required to provide interpretations to both patients and doctors, making explainability not just a technical feature but a regulatory necessity for responsible deployment in clinical settings.

Current AI systems struggle with precision in medical imaging despite high accuracy: While disease classification models achieve high accuracy rates, their bounding boxes—which pinpoint disease locations—perform poorly, often being oversized and including large areas of healthy tissue. This gap between overall accuracy and localization precision represents a significant practical problem for clinical use.

Explainability methods can improve AI performance, not just transparency: Rather than treating explainability as separate from model performance, this work demonstrates that explainable AI techniques (specifically combining heatmap and gradient analysis) can actually enhance the quality of medical diagnoses, achieving a 9% improvement in bounding box accuracy over existing methods.

Better localization reduces unnecessary clinical concern: By generating bounding boxes closer to actual disease areas rather than oversized regions, this approach could reduce false alarm areas that might cause unnecessary patient anxiety or lead to redundant follow-up procedures.

Responsible AI in healthcare requires multiple layers of validation: The research demonstrates that responsible medical AI development involves not just achieving high classification accuracy, but also ensuring that the system's explanations and localizations are precise and clinically meaningful.