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

2026-06-25 · Source: Paper Index on ACL Anthology · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Data Science & Analytics, Medical AI · Depth: Expert, quick

Summary

PromptRad is a novel knowledge-enhanced multi-label prompt-tuning approach designed for radiology report labeling in low-resource clinical settings. This method addresses the limitations of traditional rule-based labelers and conventional pre-trained language model fine-tuning, which demand extensive labeled data often unavailable. PromptRad reformulates multi-label classification as masked language modeling and integrates synonyms from the UMLS Metathesaurus into a multi-word verbalizer to enhance category representations. By fine-tuning the PLM without extra classification layers, PromptRad significantly reduces the need for labeled data. Experiments on liver CT reports demonstrate that PromptRad surpasses dictionary-based and fine-tuning baselines using only 32 labeled training examples. It also achieves performance competitive with GPT-4, despite utilizing a much smaller model, and effectively captures complex negation patterns.

Key takeaway

For NLP Engineers developing clinical text labeling systems in data-scarce environments, PromptRad offers a compelling alternative to traditional fine-tuning. You should consider adopting knowledge-enhanced prompt-tuning to achieve high accuracy with significantly fewer labeled examples, as demonstrated by its performance with only 32 liver CT reports. This approach can accelerate deployment of automated labeling for medical imaging research, even outperforming larger models like GPT-4 in specific tasks.

Key insights

Prompt-tuning with knowledge enhancement enables accurate multi-label classification in low-resource medical text.

Principles

• Reformulate classification as masked language modeling.
• Incorporate external knowledge for richer representations.
• Reduce labeled data needs via prompt-tuning.

Method

PromptRad reformulates multi-label classification as masked language modeling. It integrates UMLS Metathesaurus synonyms into a multi-word verbalizer to enrich category representations, then fine-tunes the PLM without additional classification layers.

In practice

• Apply prompt-tuning for data-scarce NLP tasks.
• Use UMLS Metathesaurus for medical synonym enrichment.
• Evaluate negation pattern capture in clinical text.

Topics

• Radiology Report Labeling
• Prompt-Tuning
• Low-Resource NLP
• Multi-Label Classification
• UMLS Metathesaurus
• Clinical Text Analysis

Code references

• ila-lab/PromptRad

Best for: AI Engineer, Machine Learning Engineer, AI Scientist, NLP Engineer, Research Scientist

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Editorial summary, takeaway, and curation by AIssential. Original article published by Paper Index on ACL Anthology.