Researchllmmedical nlpfine tuningcalibration
LLM Framework Reduces Hallucination in Medical Feature Extraction
9.0
Manal Abumelha et al. (JMIR Med Inform 2025) develop a two-phase LLM framework combining instructing fine-tuning and confidence-regularization to extract medical features from clinical notes. Trained on 700 full and 100 few-shot samples and evaluated on USMLE Step-2 Clinical Skills splits (200 public, 1,839 private), it achieved F1 scores of 0.968–0.983 (full) and 0.960–0.973 (few-shot), while reducing hallucinations by 89.9% and missing features by 88.9%.
Key Points
- 1Achieved F1 0.968–0.983 full and 0.960–0.973 few-shot on USMLE Step-2 clinical-note dataset
- 2Reduced hallucinations by 89.9% and missing features by 88.9% versus a few-shot LLM baseline
- 3Enables reliable automated clinical-note assessment with minimal training data for resource-constrained settings
Scoring Rationale
Strong empirical gains and large hallucination reduction, but scope remains focused on medical note assessment limiting generalizability.
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