The Impact of Auxiliary Patient Data on Automated Chest X-Ray Report Generation and How to Incorporate It
This work addresses the challenge of improving diagnostic accuracy in radiology reports for patients, particularly in emergency settings, by incorporating auxiliary data, though it is incremental as it builds on existing multimodal methods.
This study tackled the problem of automated chest X-ray report generation by integrating diverse patient data from health records, such as vital signs and clinical history, into multimodal language models, resulting in significantly enhanced diagnostic accuracy as demonstrated on MIMIC datasets.
This study investigates the integration of diverse patient data sources into multimodal language models for automated chest X-ray (CXR) report generation. Traditionally, CXR report generation relies solely on CXR images and limited radiology data, overlooking valuable information from patient health records, particularly from emergency departments. Utilising the MIMIC-CXR and MIMIC-IV-ED datasets, we incorporate detailed patient information such as vital signs, medicines, and clinical history to enhance diagnostic accuracy. We introduce a novel approach to transform these heterogeneous data sources into embeddings that prompt a multimodal language model; this significantly enhances the diagnostic accuracy of generated radiology reports. Our comprehensive evaluation demonstrates the benefits of using a broader set of patient data, underscoring the potential for enhanced diagnostic capabilities and better patient outcomes through the integration of multimodal data in CXR report generation.