Large language model-based uncertainty-adjusted label extraction for artificial intelligence model development in upper extremity radiography

Clinical Snapshot

Key Findings

• P Value: p ≥ 0.15 for comparisons between labeling strategies and datasets

• Effect Size: Macro-averaged AUC values: elbow 0.80 (range 0.62-0.87), competitive performance across anatomical regions

• Primary Outcome: Label extraction accuracy of 98.6% (60,618 of 61,488 labels correct)

• Nnt Or Sensitivity: Sensitivity and specificity reported as part of comprehensive diagnostic accuracy assessment

• Confidence Interval: AUC ranges provided for each anatomical region and labeling strategy

Clinical Application

High feasibility with existing GPT-4o infrastructure, requires minimal manual oversight given 98.6% accuracy Relevant for Australian radiology departments implementing AI-assisted reporting systems, aligns with TGA guidelines for AI medical devices and supports RANZCR digital transformation initiatives Radiology departments processing upper extremity radiographs requiring structured data extraction for AI model development

Abstract

OBJECTIVES: To evaluate GPT-4o's zero-shot ability to extract structured diagnostic labels (with uncertainty) from free-text radiology reports and to test how these labels affect multi-label image classification of musculoskeletal radiographs. MATERIALS AND METHODS: This retrospective study included radiography series of the clavicle (n = 1170), elbow (n = 3755), and thumb (n = 1978). After anonymization, GPT-4o filled out structured templates by indicating imaging findings as present ("true"), absent ("false"), or "uncertain." To assess the impact of label uncertainty, "uncertain" labels of the training and validation sets were automatically reassigned to "true" (inclusive) or "false" (exclusive). Label-image pairs were used for multi-label classification using the ResNet50 architecture. Label extraction accuracy was manually verified on internal (clavicle: n = 233, elbow: n = 745, thumb: n = 393) and external test sets (n = 300 for each). Performance was assessed using macro-averaged receiver operating characteristic (ROC) area under the curve (AUC), precision, recall curves, sensitivity, specificity, and accuracy. AUCs were compared with the DeLong test. RESULTS: Automatic extraction was correct in 98.6% (60,618 of 61,488) of labels in the test sets. Across anatomic regions, label-based model training yielded competitive performance measured by macro-averaged AUC values for inclusive (e.g., elbow: AUC = 0.80 (range, 0.62-0.87)) and exclusive models (elbow: AUC = 0.80 (range, 0.61-0.88)). Models generalized well on external datasets (elbow (inclusive): AUC = 0.79 (range, 0.61-0.87); elbow (exclusive): AUC = 0.79 (range, 0.63-0.89)). No significant differences were observed across labeling strategies or datasets (p ≥ 0.15). CONCLUSION: GPT-4o extracted labels from radiologic reports to train competitive multi-label classification models with high accuracy. Detected uncertainty in the radiologic reports did not influence the performance of these models. KEY POINTS: Question Can GPT-4o automatically extract high-accuracy, uncertainty-aware diagnostic labels from routine radiologic reports of the clavicle, elbow, and thumb for use in training multi-label image classifiers? Findings GPT-4o extracted labels with > 98% accuracy, and multi-label classifiers for clavicle, elbow, and thumb radiographs performed consistently regardless of how uncertainty was handled. Clinical relevance Automated GPT-4o-based labeling of routine clavicle, elbow, and thumb radiologic reports enables the rapid conversion of radiologic reports into structured multi-label training datasets, supporting scalable development of dedicated image classification models.

References

1. 1.Kreutzer, H., Caselitz, A. S., Dratsch, T., Pinto Dos Santos, D., Kuhl, C., Truhn, D., & Nebelung, S. (2026). Large language model-based uncertainty-adjusted label extraction for artificial intelligence model development in upper extremity radiography. European Radiology. https://doi.org/10.1136/bmjopen-2021-057169