Enterprise AI Analysis
Conformal Prediction for Risk-Controlled Medical Entity Extraction Across Clinical Domains
Authors: Manil Shrestha and Edward Kim
This report summarizes key findings from cutting-edge research on applying conformal prediction to Large Language Models (LLMs) for medical entity extraction, ensuring reliability and safety in clinical deployment.
Executive Impact & Key Metrics
Understanding the quantifiable impact of calibrated LLM deployments in sensitive medical contexts.
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Entities Extracted & Verified (FDA)
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Global Baseline FDR (FDA)
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GPT-4.1 Rejection Rate (RadGraph, α=0.10)
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Llama-4-Maverick Rejection Rate (RadGraph, α=0.10)
Deep Analysis & Enterprise Applications
Select a topic to dive deeper, then explore the specific findings from the research, rebuilt as interactive, enterprise-focused modules.
Overview
Overall Accuracy
FDR Control Process
Cross-Domain Comparison
Domain-Specific Calibration
Research Overview
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 based on risk-controlling prediction sets [3] that provides finite-sample false discovery rate (FDR) 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: 0.83-0.84). Our central finding is that miscalibration direction reverses across domains: on well-structured FDA labels, models are underconfident, and the global baseline FDR of 2.3% trivially satisfies α = 0.05, though per-section analysis reveals that three sections require 41-100% rejection. On free-text radiology reports, models are overconfident, and FDR control at α = 0.10 produces sharply different outcomes across models: Llama-4-Maverick rejects 19.6% of extractions while GPT-4.1 rejects 59.3%, with both models rejecting all uncertain observations. Sweep analysis across α values reveals sharp transitions in acceptance behavior that expose the baseline error structure of each domain. 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.
Overall Fact-Score Accuracy
97.7%
Overall accuracy for entities extracted from FDA drug labels, verified via FactScore.
The study processed 1,000 FDA drug labels, extracting 128,906 entities. After verification, 110,664 entities had both confidence scores and fact-score verification. A high overall accuracy of 97.7% was achieved, demonstrating the potential of LLMs for medical entity extraction. This benchmark provides a strong foundation for applying risk-controlling mechanisms.
FDR-Controlling Conformal Calibration Process
Enterprise Process Flow
Partition verified entities into calibration (50%) and test (50%) sets
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Calibrate thresholds to control False Discovery Rate (FDR)
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Select threshold Td,c such that empirical FDR does not exceed α
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Accept entities with se ≥ Td,c; flag others for human review
This four-step process outlines the rigorous methodology for implementing FDR-controlling conformal prediction. By adapting thresholds to empirical score distributions, the framework provides provable reliability bounds, crucial for high-stakes clinical applications. It ensures that the proportion of accepted but incorrect extractions remains bounded by the desired α (target FDR).
Cross-Domain & Cross-Model Calibration Comparison
| Feature | FDA Drug Labels (GPT-4.1) | Radiology Reports (GPT-4.1) | Radiology Reports (Llama-4-Maverick) |
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| Miscalibration Direction | Underconfident (ECE 0.004–0.055), curves above diagonal. Model is conservative. | Overconfident (ECE 0.102-0.525), curves below diagonal. Model assigns high confidence to incorrect extractions. | Overconfident (ECE 0.085 overall), curves below diagonal. Better calibrated than GPT-4.1. |
| Global Baseline FDR (α=0.05) | 2.3%, trivially satisfies target. All extractions accepted globally. | Cannot satisfy target (baseline 15-20% error rate). All extractions rejected globally. | Cannot satisfy target (baseline 15-20% error rate). All extractions rejected globally. |
| Rejection Rate (α=0.10) | Not applicable globally (already below α=0.05). Per-section: 41-100% for 3 sections. | 59.3% global rejection. High filtering required due to overconfidence. | 19.6% global rejection. Lower filtering needed due to better confidence discrimination. |
| Impact of Document Structure | Standardized, regulatory language leads to clearer entity boundaries and easier extraction. | Terse, variable clinical shorthand with implicit negation and hedging makes extraction harder, leading to overconfidence. | |
| Key Takeaway | Miscalibration is not a global model property; it varies by domain, document structure, and model architecture, demanding tailored calibration. | ||
This comparison highlights the critical differences in LLM calibration behavior across distinct medical domains and models. The structured nature of FDA labels leads to underconfidence, while the complex, free-text radiology reports result in overconfidence. Llama-4-Maverick demonstrates superior calibration on radiology reports, requiring significantly less rejection to meet the same FDR target compared to GPT-4.1.
Domain-Specific Calibration for Safe Clinical AI Deployment
Our findings underscore a pivotal insight for enterprise AI: LLM calibration is not a universal characteristic. The direction and magnitude of miscalibration can reverse dramatically across different clinical domains. For instance, models are underconfident on structured FDA drug labels, readily meeting strict FDR targets, while becoming overconfident on free-text radiology reports, requiring aggressive filtering.
This heterogeneity means a "one-size-fits-all" calibration strategy is inadequate. Deploying LLMs in clinical settings, where safety and reliability are paramount, demands domain-specific conformal calibration. Global thresholds can mask critical error patterns within sub-sections or specific entity types. Tailored calibration ensures that AI systems can adapt their uncertainty quantification to the unique nuances of each data source, ultimately leading to safer, more trustworthy, and more effective clinical decision support.
Businesses leveraging AI in regulated environments must adopt adaptive calibration frameworks to properly manage risks and ensure compliance. This prevents silent errors from propagating and maximizes the utility of LLMs in diverse real-world applications.
Calculate Your Potential AI ROI
Estimate the significant time and cost savings your organization could achieve by implementing calibrated AI solutions.
Annual Cost Savings
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Annual Hours Reclaimed
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Your AI Implementation Roadmap
A clear path to integrating robust, calibrated AI solutions into your enterprise.
Phase 1: Discovery & Strategy
Comprehensive assessment of your current data extraction workflows, identification of key risk areas, and definition of target FDRs for different clinical data types. Develop a tailored strategy for LLM selection and conformal prediction integration.
Phase 2: Pilot & Calibration
Implement a pilot program using your specific medical data (e.g., FDA labels, radiology reports). Apply domain-specific conformal calibration to LLMs, rigorously testing against ground truth to achieve desired FDR guarantees.
Phase 3: Integration & Monitoring
Seamless integration of the calibrated LLM solution into your existing clinical systems. Establish continuous monitoring protocols to track performance, FDR adherence, and adapt to evolving data characteristics or regulatory changes.
Phase 4: Scaling & Optimization
Expand the validated solution across more domains and use cases within your organization. Continuously optimize models and calibration strategies for improved efficiency, accuracy, and broader clinical impact.
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