Enhancing Radiology Report Generation and Visual Grounding using Reinforcement Learning

This research explores enhancing radiology report generation and visual grounding in medical imaging by applying reinforcement learning (RL) to vision-language models (VLMs). It investigates how RL, combined with task-specific feedback, can improve the quality of CXR interpretation beyond supervised fine-tuning, leading to state-of-the-art performance in generating accurate and clinically aligned reports.