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

Foundations of AI (47) Artificial Intelligence (1371) Research Methodology - AI (14)

Published on in Vol 2 (2023)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/42884, first published .
Extraction of Radiological Characteristics From Free-Text Imaging Reports Using Natural Language Processing Among Patients With Ischemic and Hemorrhagic Stroke: Algorithm Development and Validation

Extraction of Radiological Characteristics From Free-Text Imaging Reports Using Natural Language Processing Among Patients With Ischemic and Hemorrhagic Stroke: Algorithm Development and Validation

Extraction of Radiological Characteristics From Free-Text Imaging Reports Using Natural Language Processing Among Patients With Ischemic and Hemorrhagic Stroke: Algorithm Development and Validation

Authors of this article:

Enshuo Hsu1, 2 Author Orcid Image ;   Abdulaziz T Bako1 Author Orcid Image ;   Thomas Potter1 Author Orcid Image ;   Alan P Pan1 Author Orcid Image ;   Gavin W Britz3, 4 Author Orcid Image ;   Jonika Tannous1 Author Orcid Image ;   Farhaan S Vahidy1, 3, 5 Author Orcid Image
Article Authors Cited by (2) Tweetations (2) Metrics

Journals

  1. Rawas S, Tafran C, AlSaeed D. ChatGPT-powered deep learning: elevating brain tumor detection in MRI scans. Applied Computing and Informatics 2024 View
  2. Romoli M, Caliandro P. Artificial intelligence, machine learning, and reproducibility in stroke research. European Stroke Journal 2024;9(3):518 View