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This study investigates the effectiveness of large language models for extracting biomedical entities from clinical records.

Extracting Biomedical Entities from Clinical Records: A Comparative Study of Large Language Model Approaches

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This study investigates the effectiveness of large language models for extracting biomedical entities from clinical records.
Compared performance of prompt-based and fine-tuned large language models.
Evaluated models using the MACCROBAT2020 dataset and extended to QUAERO, NCBI, and E3C datasets.
Used exact and relaxed F1 scores for performance measurement across multiple entity types.
Fine-tuned LLMs achieved higher accuracy and reliable outputs than prompt-based methods and BERT baselines, especially for complex entities.
BERT-based encoders were faster and competitive under relaxed matching criteria.
Performance varied across datasets and languages, indicating the need for further tuning.

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Comparative trial evaluates large language models for named entity recognition in clinical records, suggesting advanced accuracy.

Calcina et al. (2026) studied this question.

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