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

Natural Language Processing (860) Machine Learning (1934) Electronic Health Records (1257) Clinical Information and Decision Making (1789)

Published on in Vol 11 (2023)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/46348, first published .
Deep Learning Approach for Negation and Speculation Detection for Automated Important Finding Flagging and Extraction in Radiology Report: Internal Validation and Technique Comparison Study

Deep Learning Approach for Negation and Speculation Detection for Automated Important Finding Flagging and Extraction in Radiology Report: Internal Validation and Technique Comparison Study

Deep Learning Approach for Negation and Speculation Detection for Automated Important Finding Flagging and Extraction in Radiology Report: Internal Validation and Technique Comparison Study

Authors of this article:

Kung-Hsun Weng1 Author Orcid Image ;   Chung-Feng Liu2 Author Orcid Image ;   Chia-Jung Chen3 Author Orcid Image
Article Authors Cited by (5) Tweetations (3) Metrics

Journals

  1. Su Y, Babore Y, Kahn C. A Large Language Model to Detect Negated Expressions in Radiology Reports. Journal of Imaging Informatics in Medicine 2024;38(3):1297 View
  2. Yuan H. Natural Language Processing for Chest X‐Ray Reports in the Transformer Era: BERT‐Like Encoders for Comprehension and GPT‐Like Decoders for Generation. iRADIOLOGY 2025;3(4):295 View
  3. Kulsoom U, Glavin F, Bendechache M. Natural Language Processing and Machine Learning for Analysis of Radiology Reports: A Systematic Review. IEEE Access 2025;13:112215 View
  4. Sadoune S, Richard A, Talbot F, Guyet T, Boussel L, Berry H. Automatic analysis of negation cues and scopes for medical texts in French using language models. Computers in Biology and Medicine 2025;197:110795 View
  5. Kim S, Kim D, Kim J, Koo J, Yoon J, Yoon D. In-Context Learning with Large Language Models: A Simple and Effective Approach to Improve Radiology Report Labeling. Healthcare Informatics Research 2025;31(3):295 View