Instruction-tuned generative large language models (LLMs), such as ChatGPT and Bloomz, possess excellent generalization abilities. However, they face limitations in understanding radiology reports, particularly when generating the IMPRESSIONS section from the FINDINGS section. These models tend to produce either verbose or incomplete IMPRESSIONS, mainly due to insufficient exposure to medical text data during training. We present a system that leverages large-scale medical text data for domain-adaptive pre-training of instruction-tuned LLMs, enhancing their medical knowledge and performance on specific medical tasks. We demonstrate that this system performs better in a zero-shot setting compared to several pretrain-and-finetune adaptation methods on the IMPRESSIONS generation task. Furthermore, it ranks 1st among participating systems in Task 1B: Radiology Report Summarization.

Most natural language tasks in the radiology domain use language models pre-trained on biomedical corpus. There are few pretrained language models trained specifically for radiology, and fewer still that have been trained in a low data setting and gone on to produce comparable results in fine-tuning tasks. We present RadLing, a continuously pretrained language model using ELECTRA-small architecture, trained using over 500K radiology reports that can compete with state-of-the-art results for fine tuning tasks in radiology domain. Our main contribution in this paper is knowledge-aware masking which is an taxonomic knowledge-assisted pre-training task that dynamically masks tokens to inject knowledge during pretraining. In addition, we also introduce an knowledge base-aided vocabulary extension to adapt the general tokenization vocabulary to radiology domain.