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

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
  3. Escartín J, López-Úbeda P, Martín-Noguerol T, Luna A. Role of large language models for etiological classification of brain stroke based on MRI brain reports: a feasibility study. Magnetic Resonance Imaging 2025;124:110538 View
  4. Yang Q, Jiang J, Dong X, Yang H, Wang Q, Yang Z, Yang D, Liu P. Performance of Natural Language Processing Model in Extracting Information from Free-Text Radiology Reports: A Systematic Review and Meta-Analysis. Journal of Imaging Informatics in Medicine 2025 View