Extracting structured information from unstructured histopathology reports using generative pre‐trained transformer 4 ( GPT ‐4)
Open accessAbstract
Deep learning applied to whole-slide histopathology images (WSIs) has the potential to enhance precision oncology and alleviate the workload of experts. However, developing these models necessitates large amounts of data with ground truth labels, which can be both time-consuming and expensive to obtain. Pathology reports are typically unstructured or poorly structured texts, and efforts to implement structured reporting templates have been unsuccessful, as these efforts lead to perceived extra workload. In this study, we hypothesised that large language models (LLMs), such as the generative pre-trained transformer 4 (GPT-4), can extract structured data from unstructured plain language reports using a zero-shot approach without requiring any re-training. We tested this hypothesis by utilising GPT-4 to extract information from histopathological reports, focusing on two extensive sets of pathology reports for colorectal cancer and glioblastoma. We found a high concordance between LLM-generated structured data and human-generated structured data. Consequently, LLMs could potentially be employed routinely to extract ground truth data for machine learning from unstructured pathology reports in the future. © 2023 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of The Pathological Society of Great Britain and Ireland.
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LLMs such as BERT and GPT may have zero-shot capabilities, meaning they can perform tasks without prior task-specific training.
“Due to their extensive training dataset, LLMs such as BERT and GPT may have zero-shot capabilities, meaning they can perform tasks without prior task-specific training [9].”
From Large Language Model Applications for Health Information Extraction in Oncology: Scoping Review (2025) · cites this paper as [9]
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Research works printed but not linked: 4
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- Authors
- Truhn, D., Loeffler, C. M., Müller‐Franzes, G., Nebelung, S., Hewitt, K. J., Brandner, S., Bressem, K. K., Foersch, S., & Kather, J. N.
- Affiliations
- Universitätsklinikum Aachen (DE)
- Else Kroener Fresenius Center for Digital Health Technical University Dresden Dresden Germany
- Universitätsklinikum Erlangen (DE)
- Humboldt-Universität zu Berlin (DE)
- University Medical Center of the Johannes Gutenberg University Mainz (DE)
- University of Leeds (GB)
- Corresponding author
- Kather, Jakob Nikolas
- ORCID
- Venue
- The Journal of Pathology
- Publisher
- Wiley
- Published
- 2023-12-14
- Volume
- 262
- Issue
- 3
- Pages
- 310-319
- ISSN
- 0022-3417 (ISSN-L)
- 1096-9896
- OpenAlex
- W4389792145
- Cited by
- 124
- References
- 15
- Citations by year
- 2026: 37 · 2025: 58 · 2024: 29
- FWCI
- 4.95
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- The Journal of Pathology (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Qucosa (Saxon State and University Library Dresden) (submittedVersion, cc-by)
- RWTH Publications (RWTH Aachen) (submittedVersion, other-oa)
- RWTH Publications (RWTH Aachen)
- Funders
- Deutscher Akademischer Austauschdienst (57616814)
- National Institute for Health and Care Research (NIHR213331)
- Leeds Biomedical Research Centre
- Deutsche Forschungsgemeinschaft (FO 942/2‐1, NE 2136/3‐1, TR 1700/7‐1)
- Topics
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Physical Sciences › Computer Science › Artificial Intelligence › AI in cancer detection
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Biomedical Text Mining and Ontologies
- Keywords
- GPT-4, large language models, structured data extraction, histopathology reports, zero-shot learning, colorectal cancer, glioblastoma, ground truth annotation, whole slide images, precision oncology
- MeSH terms
- Machine Learning, Glioblastoma, United Kingdom, Humans, Precision Medicine
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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