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Verfasst von:Lehnen, Nils Christian [VerfasserIn]   i
 Dorn, Franziska [VerfasserIn]   i
 Wiest, Isabella [VerfasserIn]   i
 Zimmermann, Hanna [VerfasserIn]   i
 Radbruch, Alexander [VerfasserIn]   i
 Kather, Jakob Nikolas [VerfasserIn]   i
 Paech, Daniel [VerfasserIn]   i
Titel:Data extraction from free-text reports on mechanical thrombectomy in acute ischemic stroke using ChatGPT
Titelzusatz:a retrospective analysis
Verf.angabe:Nils C. Lehnen, MD, Franziska Dorn, MD, Isabella C. Wiest, MD, MSc, Hanna Zimmermann, MD, Alexander Radbruch, MD, JD, Jakob Nikolas Kather, MD, MSc, Daniel Paech, MD, PhD
E-Jahr:2024
Jahr:April 2024
Umfang:8 S.
Illustrationen:Illustrationen
Fussnoten:Online veröffentlicht: 16. April 2024 ; Gesehen am 27.11.2024
Titel Quelle:Enthalten in: Radiology
Ort Quelle:Oak Brook, Ill. : Soc., 1923
Jahr Quelle:2024
Band/Heft Quelle:311(2024), 1 vom: Apr., Artikel-ID e232741, Seite 1-8
ISSN Quelle:1527-1315
Abstract:Background - - Procedural details of mechanical thrombectomy in patients with ischemic stroke are important predictors of clinical outcome and are collected for prospective studies or national stroke registries. To date, these data are collected manually by human readers, a labor-intensive task that is prone to errors. - - Purpose - - To evaluate the use of the large language models (LLMs) GPT-4 and GPT-3.5 to extract data from neuroradiology reports on mechanical thrombectomy in patients with ischemic stroke. - - Materials and Methods - - This retrospective study included consecutive reports from patients with ischemic stroke who underwent mechanical thrombectomy between November 2022 and September 2023 at institution 1 and between September 2016 and December 2019 at institution 2. A set of 20 reports was used to optimize the prompt, and the ability of the LLMs to extract procedural data from the reports was compared using the McNemar test. Data manually extracted by an interventional neuroradiologist served as the reference standard. - - Results - - A total of 100 internal reports from 100 patients (mean age, 74.7 years ± 13.2 [SD]; 53 female) and 30 external reports from 30 patients (mean age, 72.7 years ± 13.5; 18 male) were included. All reports were successfully processed by GPT-4 and GPT-3.5. Of 2800 data entries, 2631 (94.0% [95% CI: 93.0, 94.8]; range per category, 61%-100%) data points were correctly extracted by GPT-4 without the need for further postprocessing. With 1788 of 2800 correct data entries, GPT-3.5 produced fewer correct data entries than did GPT-4 (63.9% [95% CI: 62.0, 65.6]; range per category, 14%-99%; P < .001). For the external reports, GPT-4 extracted 760 of 840 (90.5% [95% CI: 88.3, 92.4]) correct data entries, while GPT-3.5 extracted 539 of 840 (64.2% [95% CI: 60.8, 67.4]; P < .001). - - Conclusion - - Compared with GPT-3.5, GPT-4 more frequently extracted correct procedural data from free-text reports on mechanical thrombectomy performed in patients with ischemic stroke. - - © RSNA, 2024 - - Supplemental material is available for this article.
DOI:doi:10.1148/radiol.232741
URL:Bitte beachten Sie: Dies ist ein Bibliographieeintrag. Ein Volltextzugriff für Mitglieder der Universität besteht hier nur, falls für die entsprechende Zeitschrift/den entsprechenden Sammelband ein Abonnement besteht oder es sich um einen OpenAccess-Titel handelt.

Volltext: https://doi.org/10.1148/radiol.232741
 Volltext: https://pubs.rsna.org/doi/10.1148/radiol.232741
 DOI: https://doi.org/10.1148/radiol.232741
Datenträger:Online-Ressource
Sprache:eng
K10plus-PPN:1909635545
Verknüpfungen:→ Zeitschrift

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