mlsanityData to 14 Oct 2026

High-Throughput Phenotyping of Clinical Text Using Large Language Models

arXiv 2408.01214 ↗First posted 2 Aug 2024cs.CL cs.AIcited by 3

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Abstract (arXiv)

High-throughput phenotyping automates the mapping of patient signs to standardized ontology concepts and is essential for precision medicine. This study evaluates the automation of phenotyping of clinical summaries from the Online Mendelian Inheritance in Man (OMIM) database using large language models. Due to their rich phenotype data, these summaries can be surrogates for physician notes. We conduct a performance comparison of GPT-4 and GPT-3.5-Turbo. Our results indicate that GPT-4 surpasses GPT-3.5-Turbo in identifying, categorizing, and normalizing signs, achieving concordance with manual annotators comparable to inter-rater agreement. Despite some limitations in sign normalization, the extensive pre-training of GPT-4 results in high performance and generalizability across several phenotyping tasks while obviating the need for manually annotated training data. Large language models are expected to be the dominant method for automating high-throughput phenotyping of clinical text.

Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). Builds on 20 of its 72 references that are in the map. First posted on arXiv 2 Aug 2024.

Builds on

20 of 72 references.

References · 20

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8 more
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Cited by year

Citing papers per year: 2024: 1; 2025: 2.

3 papers.

  1. 2024A Simplified Retriever to Improve Accuracy of Phenotype Normalizations by Large Language Modelscited by 3
  2. 2025From Memorization to Generalization: Fine-Tuning Large Language Models for Biomedical Term-to-Identifier Normalizationcited by 1
  3. 2025A Systematic Review on the Generative AI Applications in Human Medical Genomicscited by 0