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

Machine Learning (1952) Clinical Information and Decision Making (1802) Methods and Instruments in Medical Informatics (179) Natural Language Processing (866) Artificial Intelligence (1983) Clinical Informatics (1252) Clinical Information and Decision Making (1802) Methods and Instruments in Medical Informatics (179) Natural Language Processing (866) Artificial Intelligence (1983) Clinical Informatics (1252) Clinical Information and Decision Making (1802) Natural Language Processing (866) Artificial Intelligence (1983) Clinical Informatics (1252) Methods and Instruments in Medical Informatics (179)

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 (8) Tweetations (3) Metrics

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  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
  4. Sugimoto K, Wada S, Konishi S, Sato J, Okada K, Kido S, Tomiyama N, Matsumura Y, Takeda T. Automated Detection of Cancer-Suspicious Findings in Japanese Radiology Reports with Natural Language Processing: A Multicenter Study. Journal of Imaging Informatics in Medicine 2025 View
  5. Ohno Y, Aomori T, Nishiyama T, Kato R, Fujiki R, Ishikawa H, Kiyomiya K, Isawa M, Mochizuki M, Aramaki E, Ohtani H. Performance Improvement of a Natural Language Processing Tool for Extracting Patient Narratives Related to Medical States From Japanese Pharmaceutical Care Records by Increasing the Amount of Training Data: Natural Language Processing Analysis and Validation Study. JMIR Medical Informatics 2025;13:e68863 View
  6. Wang Y, Zhang C, Bai F, Wang Z, Qin J. FINB: a Japanese named entity recognition model based on multi-feature integration method. The Computer Journal 2025;68(4):419 View
  7. Abdikenov B, Zhaksylyk T, Shortanbaiuly O, Orazayev Y, Makhanov N, Karibekov T, Suvorov V, Imasheva A, Zhumagozhayev K, Seitova A. Future of Breast Cancer Diagnosis: A Review of DL and ML Applications and Emerging Trends for Multimodal Data. IEEE Access 2025;13:136101 View
  8. Shin C, Eom D, Lee S, Park J, Kim K, Lee K. Two stage large language model approach enhancing entity classification and relationship mapping in radiology reports. Scientific Reports 2025;15(1) View