ChexFract: From General to Specialized -- Enhancing Fracture Description Generation
This work addresses a critical gap in medical AI for clinicians by enhancing fracture reporting, though it is incremental as it builds on existing models.
The paper tackles the challenge of generating accurate radiology reports for rare pathologies like fractures in chest X-ray images, where general vision-language models often fail, and demonstrates significant improvements in fracture description accuracy by developing specialized models trained with encoders from MAIRA-2 and CheXagent.
Generating accurate and clinically meaningful radiology reports from chest X-ray images remains a significant challenge in medical AI. While recent vision-language models achieve strong results in general radiology report generation, they often fail to adequately describe rare but clinically important pathologies like fractures. This work addresses this gap by developing specialized models for fracture pathology detection and description. We train fracture-specific vision-language models with encoders from MAIRA-2 and CheXagent, demonstrating significant improvements over general-purpose models in generating accurate fracture descriptions. Analysis of model outputs by fracture type, location, and age reveals distinct strengths and limitations of current vision-language model architectures. We publicly release our best-performing fracture-reporting model, facilitating future research in accurate reporting of rare pathologies.