This research introduces a novel Set Theory-based knowledge injection framework that significantly improves the accuracy of AI-powered Chest X-ray (CXR) classification.

• Generates specialized captions for CXR images based on structured medical knowledge
• Bridges the gap between pre-trained visual models and medical imaging applications
• Demonstrates measurable improvements in cross-modality learning performance
• Provides a framework applicable to other medical imaging domains

This advancement matters for healthcare by potentially improving diagnostic accuracy, reducing physician workload, and making AI diagnostic tools more reliable in clinical settings where interpretability and accuracy are critical.

Original Paper: Enhancing Chest X-ray Classification through Knowledge Injection in Cross-Modality Learning