Conformal Prediction for Risk-Controlled Medical Entity Extraction Across Clinical Domains
arXiv:2603.00924v1 Announce Type: new 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 repor
arXiv:2603.00924v1 Announce Type: new 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.
Executive Summary
This article presents a conformal prediction framework that provides finite-sample coverage guarantees for Large Language Model (LLM)-based medical entity extraction across two clinical domains. The authors find that model calibration is domain-specific and depends on document structure, extraction category, and model architecture. The framework achieves target coverage in both settings with manageable rejection rates, motivating domain-specific conformal calibration for safe clinical deployment. The study highlights the importance of understanding model behavior in different contexts and the need for tailored calibration approaches. The results demonstrate the potential of conformal prediction in addressing the limitations of LLMs in clinical settings.
Key Points
- ▸ Conformal prediction framework provides finite-sample coverage guarantees for LLM-based medical entity extraction
- ▸ Model calibration is domain-specific and depends on document structure, extraction category, and model architecture
- ▸ The framework achieves target coverage in both settings with manageable rejection rates
Merits
Strength in methodology
The study employs a robust methodology, including the use of GPT-4.1 and Llama-4-Maverick models, as well as FactScore-based atomic statement evaluation and physician annotations for evaluation.
Demerits
Limitation in generalizability
The study is limited in its generalizability to other clinical domains and datasets, as the findings are specific to the two domains and datasets used in the study.
Expert Commentary
The study presents a significant contribution to the field of medical entity extraction and the application of conformal prediction in clinical settings. The findings highlight the importance of understanding model behavior in different contexts and the need for tailored calibration approaches. However, the study's limitations in generalizability to other clinical domains and datasets should be considered when interpreting the results. The study's implications for practical and policy applications are significant, and the use of conformal prediction in clinical settings warrants further investigation.
Recommendations
- ✓ Future studies should investigate the application of conformal prediction in other clinical domains and datasets to improve the generalizability of the findings.
- ✓ Researchers should continue to explore the development of domain-specific conformal calibration approaches to improve the safety and reliability of LLM-based medical entity extraction in clinical settings.