Accessibility settings

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

Artificial Intelligence (2458) Clinical Information and Decision Making (2149) New Methods (1201) Natural Language Processing (995) Decision Support for Health Professionals (1693) Imaging Informatics (351) Machine Learning (2307) Stroke (277) Development and Evaluation of Research Methods, Instruments and Tools (855) Generative Language Models Including ChatGPT (883)

Published on in Vol 27 (2025)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/48328, first published .
Doctor viewing brain scans on a large screen, "LANGUAGE MODEL" text visible

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
Article Authors Cited by (7) Tweetations Metrics

Journals

  1. Owens D, Nguyen D, Dohopolski M, Rousseau J, Peterson E, Navar A. Accuracy of Large Language Models to Identify Stroke Subtypes Within Unstructured Electronic Health Record Data. Stroke 2025;56(10):2966 View
  2. Lee R, Hadidchi R, Coard M, Rubinov Y, Alamuri T, Liaw A, Chandrupatla R, Duong T. Use of Large Language Models on Radiology Reports: A Scoping Review. Journal of the American College of Radiology 2026;23(3):437 View
  3. Guzik A, Fraser J, Southerland A, Vagal A, Tsai J, Dumitrascu O, Nystrom K, Martinez Johnson M, Hess D, Jayaraman M. Creating Virtual Stroke Networks: Current and Future Role of Artificial Intelligence, Mobile Imaging Applications, and Telehealth in Triage and Treatment of Acute Ischemic Stroke: A Scientific Statement From the American Heart Association. Stroke 2026;57(3) View
  4. Saji J, Sreekumar A, Abraham S, Antony J. Evaluation of large language models in responding to healthcare queries: A comparison of AI performance for cerebrovascular accident and snakebite. Artificial Intelligence in Emergency Medicine 2026;2:100020 View
  5. Duarte K, Sidhu A, Bakshi M, de Barros M, Karmur B, Aslan T, Zhang D, AlShamrani M, Qiu W, Menon B. Systematic Review of Large Language Models and Natural Language Processing in Stroke Care: Applications, Challenges, and Future Directions. Stroke: Vascular and Interventional Neurology 2026;6(3) View
  6. Viculin I, Vrdoljak J, Tomić K, Canjko I, Čerina Pavlinović D, Ćurin M, Bošković L, Družijanić Z, Vuković B, Popović V, Mladinov S, Božić J, Vrdoljak E. Evaluating Large Reasoning Models Versus Human Multidisciplinary Teams in Lung Cancer Decision-Making: A Real-World Study (Preprint). Journal of Medical Internet Research 2026 View

Conference Proceedings

  1. Chappidi S, Singh J, Valentina Krauze A. Proceedings of the 2026 CHI Conference on Human Factors in Computing Systems. Who Does What? Archetypes of Roles Assigned to LLMs During Human-AI Decision-Making View