This research introduces a novel approach to medical multimodal LLMs that mimics how doctors analyze images by focusing on specific regions rather than the entire image at once.

• Develops a region-aware medical MLLM that can identify which specific image areas it focuses on when generating responses
• Incorporates bilingual capabilities (English and Chinese) to increase accessibility across healthcare systems
• Achieves superior performance on diverse biomedical tasks including medical image analysis, report generation, and visual question answering
• Provides enhanced interpretability by explicitly showing which image regions inform each generated sentence

This advancement matters for healthcare because it aligns AI reasoning with clinical workflows, potentially improving diagnostic accuracy while making AI decisions more transparent and trustworthy for medical professionals.

Original Paper: Interpretable Bilingual Multimodal Large Language Model for Diverse Biomedical Tasks