Leveraging General-Domain Language Models for Robust Multimodal Radiology Analysis with Minimal Training

A novel framework, MID-M, leverages the in-context learning capabilities of a general-domain large language model to process multimodal radiology data efficiently, achieving comparable or superior performance to task-specific and heavily pre-trained models, while demonstrating exceptional robustness against data quality issues.