Automatic Generation of Medical Imaging Diagnostic Report using BLIP Model

The generation of radiology reports forms a very important aspect of medical imaging, which furnishes physicians with timely and accurate diagnostic information. However, interpreting medical images and compiling their findings remains cumbersome for radiologists. To address this challenge, we propose an automatic report generation system based on the BLIP (Bootstrapping Language-Image Pre-training) model. By fine-tuning a pre-trained BLIP image captioning framework using paired chest X-ray images and their corresponding diagnostic reports, the model learns to describe visual findings in clear and clinically relevant text. Experimental results show that our model achieves strong performance with BLEU, METEOR, ROUGE, and RadGraph-F1 scores of 0.5859, 0.4021, 0.6780, and 0.6424 respectively, demonstrating its ability to generate meaningful and clinically grounded descriptions. This work highlights the potential of vision–language models in assisting radiologists and improving the efficiency of clinical workflows.