This paper introduces a radiology-focused visual language model designed to generate radiology reports from chest X-rays. Building on previous findings that large language models can acquire multimodal capabilities when aligned with pretrained vision encoders, we demonstrate similar potential with chest X-ray images. The model combines an image encoder (CLIP) with a fine-tuned large language model (LLM) based on the Vicuna-7B architecture. The training process involves a two-stage approach: initial alignment of chest X-ray features with the LLM, followed by fine-tuning for radiology report generation. The study highlights the importance of generating both FINDINGS and IMPRESSIONS sections in radiology reports and evaluates the model’s performance using various metrics, achieving notable accuracy in generating high-quality medical reports. The research also addresses the need for domain-specific fine-tuning to capture the intricate details necessary for accurate medical interpretations and reports.

This paper presents the UoG Siephers team participation at the Discharge Me! Shared Task on Streamlining Discharge Documentation. For our participation, we investigate appropriately selecting and encoding specific sections of Electronic Health Records (EHR) as input data for sequence-to-sequence models, to generate the discharge instructions and brief hospital course sections of a patient’s EHR. We found that, despite the large volume of disparate information that is often available in EHRs, selectively choosing an appropriate EHR section for training and prompting sequence-to-sequence models resulted in improved generative quality. In particular, we found that using only the history of present illness section of an EHR as input often led to better performance than using multiple EHR sections.

Relation extraction methods are essential for creating robust text mining tools to help researchers find useful knowledge in the vast published literature. Easy-to-use and generalizable methods are needed to encourage an ecosystem in which researchers can easily use shared resources and build upon each others’ methods. We present the Kindred Python package for relation extraction. It builds upon methods from the most successful tools in the recent BioNLP Shared Task to predict high-quality predictions with low computational cost. It also integrates with PubAnnotation, PubTator, and BioNLP Shared Task data in order to allow easy development and application of relation extraction models.