BLIX-Ray: Chest X-Ray Report Generation with Vision-Language Models and Reinforcement Learning

The radiology report is essential for directing the course of treatment for a patient. However, writing radiology reports is a huge burden for radiologists. Manual reporting faces challenges due to intra-labeler and inter-labeler variability, causing inconsistent patient treatment and diagnostic results, and complicating matters due to alertness and changing judgment. To address these challenges, we propose an automated radiology report generation model tailored for chest X-ray imaging. We introduce BLIX-Ray, a vision-language model (VLM) designed to generate radiology reports using a transfer learning approach. BLIX-Ray fine-tunes the pre-trained BLIP model to extract and align rich visual and textual representations suited to the medical imaging domain. We use reinforcement learning (RL) to directly optimize report generation based on language evaluation metrics in order to further enhance performance. We evaluate BLIX-Ray on two publicly available datasets: IU-Xray and CheXpert Plus. Experimental results demonstrate that our model achieves the highest CIDEr and BERTScore on both datasets, while n-gram-oriented baselines retain an advantage on BLEU. BLIX-Ray integrates reinforcement learning and transfer learning to improve medical report generation under limited and heterogeneous data conditions. The results highlight the potential of vision-language models to support automated and standardized radiology reporting. Notably, BLIX-Ray achieves the best reported CIDEr (0.6534) and BERTScore (0.8765) among the compared methods on IU-Xray and leads on CIDEr, METEOR, and BERTScore on CheXpert Plus (BERTScore 0.8407), demonstrating strong report-level consensus and semantic similarity with the reference reports.