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

Machine Learning (1501) Clinical Information and Decision Making (1434) Methods and Instruments in Medical Informatics (135) Natural Language Processing (706) Artificial Intelligence (1421) Clinical Informatics (1050) Clinical Information and Decision Making (1434) Methods and Instruments in Medical Informatics (135) Natural Language Processing (706) Artificial Intelligence (1421) Clinical Informatics (1050) Clinical Information and Decision Making (1434) Natural Language Processing (706) Artificial Intelligence (1421) Clinical Informatics (1050) Methods and Instruments in Medical Informatics (135)

Published on in Vol 11 (2023)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/49041, first published .
Extracting Clinical Information From Japanese Radiology Reports Using a 2-Stage Deep Learning Approach: Algorithm Development and Validation

Extracting Clinical Information From Japanese Radiology Reports Using a 2-Stage Deep Learning Approach: Algorithm Development and Validation

Extracting Clinical Information From Japanese Radiology Reports Using a 2-Stage Deep Learning Approach: Algorithm Development and Validation

Authors of this article:

Kento Sugimoto1 Author Orcid Image ;   Shoya Wada1, 2 Author Orcid Image ;   Shozo Konishi1 Author Orcid Image ;   Katsuki Okada1 Author Orcid Image ;   Shirou Manabe1, 2 Author Orcid Image ;   Yasushi Matsumura1, 3 Author Orcid Image ;   Toshihiro Takeda1 Author Orcid Image
Article Authors Cited by (3) Tweetations (3) Metrics

Journals

  1. Li Y, Luan Z, Liu Y, Liu H, Qi J, Han D. Automated information extraction model enhancing traditional Chinese medicine RCT evidence extraction (Evi-BERT): algorithm development and validation. Frontiers in Artificial Intelligence 2024;7 View
  2. Zhong Z, Xie X. Clinical applications of generative artificial intelligence in radiology: image translation, synthesis, and text generation. BJR|Artificial Intelligence 2024;1(1) View
  3. Sato J, Sugimoto K, Suzuki Y, Wataya T, Kita K, Nishigaki D, Tomiyama M, Hiraoka Y, Hori M, Takeda T, Kido S, Tomiyama N. Annotation-free multi-organ anomaly detection in abdominal CT using free-text radiology reports: a multi-centre retrospective study. eBioMedicine 2024;110:105463 View