RadVLM is a multitask conversational foundation model specifically designed for chest X-ray interpretation, addressing the growing radiologist shortage while enhancing diagnostic capabilities.

• Supports interactive diagnostic conversations beyond simple report generation
• Combines multiple capabilities including abnormality detection and report generation in one compact model
• Designed to integrate into clinical workflows as an AI assistant for radiologists
• Serves educational purposes by explaining findings and reasoning to users

This innovation matters because it moves radiology AI from single-task tools to versatile assistants that can engage in natural dialogue about medical images, potentially improving workflow efficiency and supporting clinical decision-making in resource-constrained environments.

RadVLM: A Multitask Conversational Vision-Language Model for Radiology