Conformal Prediction for Risk-Controlled Medical Entity Extraction Across Clinical Domains

arXiv:2603.00924v2 Announce Type: replace-cross
Abstract: 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 (entity F1: 0.81 to 0.84). Our central finding is that miscalibration direction reverses across domains: on well-structured FDA labels, models are underconfident, requiring modest conformal thresholds ($tau approx 0.06$), while on free-text radiology reports, models are overconfident, demanding strict thresholds ($tau$ up to 0.99). Despite this heterogeneity, conformal prediction achieves target coverage ($geq 90%$) in both settings with manageable rejection rates (9–13%). These results demonstrate that calibration is not a global model property but depends on document structure, extraction category, and model architecture, motivating domain-specific conformal calibration for safe clinical deployment.