Reducing Annotation Costs Using Knowledge Distillation for Label‑Efficient Negation Detection in Radiology Reports:...

Negation in radiology reports (eg, "no evidence of dissection") is essential for accurately conveying diagnostic information. Clinical natural language processing systems, however, often fail to determine whether findings occur within negated or speculative contexts, leading to excessive false keyword alerts, noise in information extraction, and reduced precision in downstream clinical or research applications. Transformer models have advanced detection capabilities but require costly expert labeling. Large language models (LLMs) may provide label-efficient alternatives through few-shot prompting or knowledge distillation.

This study aims to systematically evaluate LLMs and transformer models under different supervision strategies, including comparisons of expert-labeled and LLM-distilled transformers, teacher LLMs (GPT-5.3 Chat, Gemma 4, and Qwen3.6-Plus), sampling approaches, and direct few-shot LLM inference, to determine whether LLM inference or LLM-based distillation can substitute for human labeling in radiology-report negation detection while reducing annotation costs.

We reused and analyzed 6000 deidentified English-language radiology reports from 3 hospital branches. Three LLMs-GPT-5.3 Chat, Gemma 4, and Qwen3.6-Plus-performed 0-, 5-, and 10-shot inference to identify negations and speculations unrelated to abnormal findings and generate pseudo-labels for transformer fine-tuning (Bio_ClinicalBERT [Bio + Clinical BERT model], BERT [Bidirectional Encoder Representations from Transformers], and ALBERT [A Lite Bidirectional Encoder Representations from Transformers]). We trained models using human-labeled or LLM-distilled supervision across 28 training data scales (n=42-4200) and 3 data sampling seeds and compared them across multiple supervision and training conditions encompassing backbone, label source, and sampling strategy. We assessed performance using keyword-level precision, recall, and F1-score with paired bootstrap tests (10,000 replicates).

We refined 1000 report annotations (interrater κ=0.957) and fine-tuned 1260 transformer models. Human-labeled transformers consistently outperformed distillation-labeled ones, with the largest gap at small training scales (ΔF1-score ≈0.042; adjusted P<.001), which narrowed at larger scales (ΔF1-score ≈0.022; adjusted P<.001). Teacher model choice had minimal effect on distilled performance. All 3 human-labeled transformer backbones (BERT, ALBERT, and Bio_ClinicalBERT) achieved high keyword-level F1-scores (0.982-0.985 under their best conditions; n=4200). In the 10-shot setting, GPT-5.3 Chat (F1-score 0.974) modestly underperformed human-labeled transformers in the large-sample range (ΔF1-score=0.009; adjusted P=.047) and significantly outperformed Gemma 4 (F1-score 0.957; adjusted P=.01) and Qwen3.6-Plus (F1-score 0.950; adjusted P<.001) in the bootstrap analysis. GPT-5.3 Chat labeled reports at approximately US $0.018 per report and became less expensive than manual annotation when expert hourly wages exceeded US $0.64-1.50.

Human-supervised transformers remain the strongest baseline, but frontier LLMs such as GPT-5.3 Chat offer comparable performance with minimal supervision. LLM-based distillation offers a cost-effective alternative with only minor performance loss. Dataset scale proved more influential than sampling strategy. Additionally, superior teacher performance yielded no proportional gains through distillation, as open-weight or low-cost teachers achieved comparable results. If supported by external validation, the proposed label-efficient framework could enable accurate and affordable negation detection and rapid domain adaptation.