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

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 2025 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 2025 View