ArticleJournal of the American Medical Informatics Association : JAMIA2024
Disambiguation of acronyms in clinical narratives with large language models.
Article in Journal of the American Medical Informatics Association : JAMIA, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- End-to-end pipeline for automated heart failure diagnosis with clinical notes using SNOMED-CT.Scientific reports · 2026Article
- Applications of Large Language Models in Medical Research: From Systematic Reviews to Clinical Studies.Bioengineering (Basel, Switzerland) · 2026Review
- Safety and efficacy of privacy-preserving models to create Lay summaries of brain MRI reports.Scientific reports · 2026Article
- A Comprehensive Polish Medical Speech Dataset for Enhancing Automatic Medical Dictation.Scientific data · 2025Article
- Deciphering Abbreviations in Malaysian Clinical Notes Using Machine Learning.Methods of information in medicine · 2024Article
- Large language models in biomedicine and health: current research landscape and future directions.Journal of the American Medical Informatics Association : JAMIA · 2024Article
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3 authors.
Funding
Abstract
objectiveTo assess the performance of large language models (LLMs) for zero-shot disambiguation of acronyms in clinical narratives. MATERIALS AND
methodsClinical narratives in English, German, and Portuguese were applied for testing the performance of four LLMs: GPT-3.5, GPT-4, Llama-2-7b-chat, and Llama-2-70b-chat. For English, the anonymized Clinical Abbreviation Sense Inventory (CASI, University of Minnesota) was used. For German and Portuguese, at least 500 text spans were processed. The output of LLM models, prompted with contextual information, was analyzed to compare their acronym disambiguation capability, grouped by document-level metadata, the source language, and the LLM.
resultsOn CASI, GPT-3.5 achieved 0.91 in accuracy. GPT-4 outperformed GPT-3.5 across all datasets, reaching 0.98 in accuracy for CASI, 0.86 and 0.65 for two German datasets, and 0.88 for Portuguese. Llama models only reached 0.73 for CASI and failed severely for German and Portuguese. Across LLMs, performance decreased from English to German and Portuguese processing languages. There was no evidence that additional document-level metadata had a significant effect.
conclusionFor English clinical narratives, acronym resolution by GPT-4 can be recommended to improve readability of clinical text by patients and professionals. For German and Portuguese, better models are needed. Llama models, which are particularly interesting for processing sensitive content on premise, cannot yet be recommended for acronym resolution.
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