ArticleAMIA ... Annual Symposium proceedings. AMIA Symposium2024
Evaluating the Performance of Large Language Models for Named Entity Recognition in Ophthalmology Clinical Free-Text Notes.
Article in AMIA ... Annual Symposium proceedings. AMIA Symposium, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Extracting Medical Information From Unstructured Clinical Text Using Large Language Models to Enhance Health Care Interoperability: Proof-of-Concept Study.Journal of medical Internet research · 2026Article
- SynthEHR-eviction: enhancing eviction SDoH detection with LLM-augmented synthetic EHR data.NPJ digital medicine · 2026Article
- Prediction of Chronic Obstructive Pulmonary Disease Using Machine Learning, Clinical Summary Notes, and Vital Signs: A Single-Center Retrospective Cohort Study in the United States.Advances in respiratory medicine · 2026Article
- Opinion on "Artificial intelligence in sepsis early prediction and diagnosis using unstructured data in healthcare".Frontiers in immunology · 2025Article
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Authors and funding
4 authors.
Funding
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Abstract
This study compared large language models (LLMs) and Bidirectional Encoder Representations from Transformers (BERT) models in identifying medication names, routes, and frequencies from publicly available free-text ophthalmology progress notes of 480 patients. 5,520 lines of annotated text were divided into train (N=3,864), validation (N=1,104), and test sets (N=552). We evaluated ChatGPT-3.5, ChatGPT-4, PaLM 2, and Gemini to identify these medication entities. We fine-tuned BERT, BioBERT, ClinicalBERT, DistilBERT, and RoBERTa for the same task using the training set. On the test set, GPT-4 achieved the best performance (macro-averaged F1 0.962). Among the BERT models, BioBERT achieved the best performance (macro-averaged F1 0.875). Modern LLMs outperformed BERT models even in the highly domain-specific task of identifying ophthalmic medication information from progress notes, showcasing the potential of LLMs for medical named entity recognition to enhance patient care.
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40417582PMC12099357What OpenQuestion holds
Registered trials
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