This paper is in the following e-collection/theme issue:

Artificial Intelligence (2162) Clinical Information and Decision Making (1935) Natural Language Processing (923) Machine Learning (2095) Development and Evaluation of Research Methods, Instruments and Tools (756) Decision Support for Health Professionals (1555) Stroke (248) Imaging Informatics (308) Generative Language Models Including ChatGPT (725) New Methods (1140)

Published on in Vol 27 (2025)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/48328, first published .
Large Language Models–Supported Thrombectomy Decision-Making in Acute Ischemic Stroke Based on Radiology Reports: Feasibility Qualitative Study

Large Language Models–Supported Thrombectomy Decision-Making in Acute Ischemic Stroke Based on Radiology Reports: Feasibility Qualitative Study

Large Language Models–Supported Thrombectomy Decision-Making in Acute Ischemic Stroke Based on Radiology Reports: Feasibility Qualitative Study

Authors of this article:

Jonathan Kottlors1 Author Orcid Image ;   Robert Hahnfeldt1 Author Orcid Image ;   Lukas Görtz1 Author Orcid Image ;   Andra-Iza Iuga1 Author Orcid Image ;   Philipp Fervers1 Author Orcid Image ;   Johannes Bremm1 Author Orcid Image ;   David Zopfs1 Author Orcid Image ;   Kai R Laukamp1 Author Orcid Image ;   Oezguer A Onur2 Author Orcid Image ;   Simon Lennartz1 Author Orcid Image ;   Michael Schönfeld1 Author Orcid Image ;   David Maintz1 Author Orcid Image ;   Christoph Kabbasch1 Author Orcid Image ;   Thorsten Persigehl1 Author Orcid Image ;   Marc Schlamann1 Author Orcid Image
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