Deep sight: enhancing periprocedural adverse event recording in endoscopy by structuring text documentation with privacy-preserving large language models

Applying LLMs to extract structured medical information, particularly from endoscopy reports, offers an efficient, scalable, and adaptable documentation method that captures adverse events accurately with a low error rate. It facilitates immediate quality reporting and reduces manual documentation efforts.

Authors

Isabella C. Wiest (ORCID: https://orcid.org/0009-0004-4848-614X)
Sebastian Belle
Dyke Ferber (ORCID: https://orcid.org/0009-0006-6195-9276)
Jakob Nikolas Kather (ORCID: https://orcid.org/0000-0002-3730-5348)
Matthias P. Ebert (ORCID: https://orcid.org/0000-0003-3228-5138)
Stefan Wittlinger

Institutions

Heidelberg University (DE)
University Hospital Heidelberg (DE)
National Center for Tumor Diseases (DE)
European Molecular Biology Laboratory (DE)
University Medical Centre Mannheim (DE)
University Hospital Carl Gustav Carus (DE)

Publication Details

Journal: iGIE
Published: 2024-08-23
DOI: https://doi.org/10.1016/j.igie.2024.08.001
Citations: 7
Primary Topic: Digital Imaging in Medicine
Type: article
Field-Weighted Citation Impact: 3.80

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article

Deep sight: enhancing periprocedural adverse event recording in endoscopy by structuring text documentation with privacy-preserving large language models

Isabella C. Wiest, Sebastian Belle, Dyke Ferber, Jakob Nikolas Kather et al.

Aug 23, 2024

7 citations

iGIE

Digital Imaging in Medicine

3.80

article

Deep sight: enhancing periprocedural adverse event recording in endoscopy by structuring text documentation with privacy-preserving large language models

Isabella C. Wiest, Sebastian Belle, Dyke Ferber, Jakob Nikolas Kather, Matthias P. Ebert, Stefan Wittlinger

August 23, 2024article en

7 citations

Abstract

Applying LLMs to extract structured medical information, particularly from endoscopy reports, offers an efficient, scalable, and adaptable documentation method that captures adverse events accurately with a low error rate. It facilitates immediate quality reporting and reduces manual documentation efforts.

iGIEVol. 3(4)

Heidelberg University (DE), University Hospital Heidelberg (DE), National Center for Tumor Diseases (DE), European Molecular Biology Laboratory (DE), University Medical Centre Mannheim (DE), University Hospital Carl Gustav Carus (DE)

Openalex Percentile: Top 6%

Digital Imaging in Medicine

3.80

DOI Open Access PDF OpenAlex

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Deep sight: enhancing periprocedural adverse event recording in endoscopy by structuring text documentation with privacy-preserving large language models — Isabella C. Wiest, Sebastian Belle, et al. · iGIE (2024) | TGRS Research Map | TGRS