{
  "abstract": "Introduction\n\nLarge language models (LLMs) are being explored for multiple applications in medical research, including medical text classification. Authors of the study evaluated the performance of three off-the-shelf LLMs in classifying unstructured free-text CT pulmonary angiography reports for pulmonary embolism (PE)-related diagnostic labels.Methods\n\nAuthors assessed human-labeled CT reports using three LLMs (ChatGPT-4o, Llama 3.3 70B, and Llama 3.1 8B) with zero-shot simple and chain-of-thought (CoT) prompts. Classification tasks included acute PE (N = 1,025) and, among reports with PE (n = 660), bilateral PE and large PE.Results\n\nLLMs demonstrated high classification performance for acute PE and bilateral PE but lower performance for large PE. With simple prompts, the highest F1 scores were 97.0% for acute PE (Llama 3.1 8B; sensitivity, 95.6%; specificity, 97.3%), 92.9% for bilateral PE (ChatGPT-4o; sensitivity, 98.0%; specificity, 88.6%), and 72.8% for large PE (ChatGPT-4o; sensitivity, 82.9%; specificity, 82.2%). CoT prompting did not meaningfully improve acute PE classification but provided slight improvements in subclassification performance for bilateral and large PE.Conclusions\n\nOff-the-shelf LLMs can classify free-text CT reports using simple prompt engineering and may facilitate automated data extraction for medical research following appropriate evaluation of classification performance.",
  "abstract_shown": true,
  "affiliations": [
    "The University of Kansas School of Medicine-Kansas City, Kansas City, KS.",
    "Department of CardiovascularMedicine.",
    "Department of Biostatistics and Data Science.",
    "Department of Medical Informatics.",
    "Department of Electrical Engineering and Computer Science, The University of Kansas, Lawrence, KS, USA."
  ],
  "article_type": "research article",
  "arxiv": null,
  "author_total": 10,
  "authors": [
    {
      "affs": [
        0,
        1
      ],
      "name": "Amulya Gupta"
    },
    {
      "affs": [
        0
      ],
      "name": "Alex Hongslo"
    },
    {
      "affs": [
        0
      ],
      "name": "Quynh Nguyen"
    },
    {
      "affs": [
        0
      ],
      "name": "Jake Caldwell"
    },
    {
      "affs": [
        0
      ],
      "name": "Ben Choi"
    },
    {
      "affs": [
        0,
        1
      ],
      "name": "Christopher J Harvey"
    },
    {
      "affs": [
        0,
        2
      ],
      "name": "Jeffrey Thompson"
    },
    {
      "affs": [
        0,
        3
      ],
      "name": "Diego Mazzotti"
    },
    {
      "affs": [
        4
      ],
      "name": "Zijun Yao"
    },
    {
      "affs": [
        0
      ],
      "name": "Amit Noheria"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/5a1j59zbztwfp.bib",
  "cited_by": 0,
  "doi": "10.17161/kjm.vol19.25396",
  "fulltext_url": "https://europepmc.org/articles/PMC13694176",
  "id": "5a1j59zbztwfp",
  "issns": [
    "1948-2035"
  ],
  "landing_url": "https://europepmc.org/article/MED/42921394",
  "language": "eng",
  "licence": "CC-BY-NC-ND",
  "licence_label": "CC BY-NC-ND",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by-nc-nd/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "openalex",
      "1702|Machine Learning in Healthcare"
    ]
  ],
  "openalex": "W7222651799",
  "pdf_url": "https://europepmc.org/articles/PMC13694176?pdf=render",
  "pmcid": "PMC13694176",
  "pmid": "42921394",
  "published": "2026-09-01",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/5a1j59zbztwfp.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:21:02Z",
      "native_id": "MED:42921394",
      "source": "europepmc",
      "url": "https://europepmc.org/article/MED/42921394"
    }
  ],
  "subjects": [
    {
      "field": "medicine",
      "origin": "journal-openalex:F27",
      "subfield": "general"
    }
  ],
  "title": "Zero-Shot Extraction of Structured Clinical Data from Free-Text CT Reports Using Foundational Large Language Models.",
  "title_text": "Zero-Shot Extraction of Structured Clinical Data from Free-Text CT Reports Using Foundational Large Language Models.",
  "type": "peer-reviewed",
  "updated_at": "2026-10-30T12:21:02Z",
  "url": "https://research.draconitic.com/paper/5a1j59zbztwfp",
  "venue": "Kansas journal of medicine",
  "versions": []
}