PromptRad: Knowledge-Enhanced Multi-Label Prompt-Tuning for Low-Resource Radiology Report Labeling

Automatic report labeling facilitates the identification of clinical findings from unstructured text and enables large-scale annotation for medical imaging research.Existing rulebased labelers struggle with the diverse descriptions in clinical reports, while fine-tuning pretrained language models (PLMs) requires large amounts of labeled data that are often unavailable in clinical settings.In this paper, we propose PromptRad, a knowledge-enhanced multilabel prompt-tuning approach for radiology report labeling under low-resource settings.PromptRad reformulates multi-label classification as masked language modeling and incorporates synonyms from the UMLS Metathesaurus into a multi-word verbalizer to enrich category representations.By fine-tuning the PLM without additional classification layers, PromptRad requires substantially less labeled data than conventional fine-tuning.Experiments on liver CT (computed tomography) reports show that PromptRad outperforms dictionary-based and fine-tuning baselines with only 32 labeled training examples, and achieves competitive performance with GPT-4 despite using a much smaller model.Further analysis demonstrates that PromptRad captures complex negation patterns more effectively than existing methods, making it a promising solution for report labeling in data-scarce clinical scenarios.