DALL-M is a novel framework that generates synthetic data to enhance clinical X-ray datasets with vital contextual information, improving diagnostic accuracy when images alone are insufficient.

• Augments structured patient data (vital signs, clinical features) alongside radiology reports
• Creates contextually coherent synthetic data to address dataset limitations
• Enables more comprehensive analysis by integrating clinical context with X-ray images
• Improves predictive modeling for more accurate medical diagnostics

This research addresses a critical gap in medical imaging, where radiologists often need additional clinical context beyond X-rays for accurate diagnosis. By leveraging LLMs to generate high-quality synthetic data, DALL-M offers a pathway to enhance clinical decision-making and diagnostic precision in healthcare settings.

DALL-M: Context-Aware Clinical Data Augmentation with LLMs