Large Language Models for Code Generation

Traditional medical reports typically require doctors to manually draft the content, a process that is both time-consuming and prone to human error, resulting in inconsistencies in the report content. By integrating computer vision and large language model technologies, we can achieve the automated generation of medical reports. Computer vision technology can automatically analyze medical images, detect lesions or abnormal structures, and provide accurate image analysis results. Subsequently, language models can use these results to automatically generate structured medical reports. These reports not only accurately describe abnormalities in the images but also offer diagnostic suggestions and treatment plans. This innovative technology can significantly reduce the workload for doctors, improve the accuracy and consistency of reports, enhance the efficiency of medical services, lower healthcare costs, and improve the overall patient experience. 

Ground Truth: The ET tube is 3.5 cm above the carina.  The NG tube tip is off the film, at least in the stomach.  Right IJ Cordis tip is in the proximal SVC. The heart size is moderately enlarged.  There is ill-defined vasculature and alveolar infiltrate, right greater than left.  This is markedly increased compared to the film from two hours prior and likely represents fluid overload.
Generated report: The endotracheal tube terminates approximately 3 cm above the carina. enteric tube courses below the diaphragm out of the field of view. there are low lung volumes. bilateral perihilar opacities are worrisome for moderate pulmonary edema. no large pleural effusion is seen. there is no evidence of pneumothorax. the cardiac silhouette is mildly enlarged.