Conformal Prediction for Risk-Controlled Medical Entity Extraction Across Clinical Domains
Large Language Models (LLMs) are increasingly used for medical entity extraction, yet their confidence scores are often miscalibrated, limiting safe deployment in clinical settings. We present a conformal prediction framework that provides finite-sample coverage guarantees for LLM-based extraction across two clinical domains. First, we extract structured entities from 1,000 FDA drug labels across eight sections using GPT-4.1, verified via FactScore-based atomic statement evaluation (97.7\% accuracy over 128,906 entities). Second, we extract radiological entities from MIMIC-CXR reports using the RadGraph schema with GPT-4.1 and Llama-4-Maverick, evaluated against physician annotations (ent...
| Authors | Manil Shrestha, Edward Kim |
|---|---|
| Primary category | cs.CL |
| Published | 1 March 2026 |
| Last updated at source | 9 March 2026 |
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