A framework for extraction of clinical information from radiological mammography reports using large language models and retrieval augmented generation
Abstract
Background: Extracting structured information from free-text radiology reports is essential for downstream clinical analysis and decision support. In mammography, this challenge is amplified by heterogeneous writing styles, the absence of standardized terminology across institutions, and limited availability of annotated Spanish datasets. Large language models (LLMs) offer a compelling alternative by enabling few-shot generalization without task-specific fine-tuning.
Methods: We propose a structured Retrieval-Augmented Generation (RAG) framework that leverages annotated mammography reports as in-context demonstrations, enabling joint named entity and relation extraction without task-specific fine-tuning. The framework is evaluated on Spanish BI-RADS-style mammography reports and achieves performance comparable to fine-tuned BETO models in low-resource settings, while significantly reducing training and deployment overhead.
Results: In NER, GPT-based few-shot models achieved competitive performance (0.89-0.94 F[Formula: see text]) relative to the fine-tuned BETO baseline (0.97 F[Formula: see text]), despite requiring no task-specific training. In RE, LLMs showed moderate performance (up to 0.78 F[Formula: see text]), remaining below the supervised BETO model (0.99 F[Formula: see text]), reflecting the greater sensitivity of RE to boundary errors and cross-sentence context. Inference costs were low (fractions of a cent per report) and latency remained within seconds, enabling…
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