ArticleBMC medical informatics and decision making2021
Automatic detection of actionable radiology reports using bidirectional encoder representations from transformers.
Article in BMC medical informatics and decision making, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.
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Who cites it
21 citing papers in PubMed, 42 citations in OpenAlex.
- From zero-shot to fine-tuning: optimize large language models for error detection of ultrasound reports.Insights into imaging · 2026Article
- Identification of high-priority radiology reports with unexpected findings using fine-tuned large language models.European radiology · 2026Article
- ModernBERT is more efficient than conventional BERT for chest CT findings classification in Japanese radiology reports.Scientific reports · 2026Article
- Privacy-Preserving Large Language Model for Matching Findings and Tracking Interval Changes in Longitudinal Radiology Reports.Journal of imaging informatics in medicine · 2026Article
- Automated Detection of Cancer-Suspicious Findings in Japanese Radiology Reports with Natural Language Processing: A Multicenter Study.Journal of imaging informatics in medicine · 2025Article
- Automated Incidental Findings Notification Through the Electronic Health Record Utilizing Dictation Macros.Journal of imaging informatics in medicine · 2025Article
- Classification of Interventional Radiology Reports into Technique Categories with a Fine-Tuned Large Language Model.Journal of imaging informatics in medicine · 2025Article
- Fine-tuned large Language model for extracting newly identified acute brain infarcts based on computed tomography or magnetic resonance imaging reports.Emergency radiology · 2025Article
- Automatic Abstraction of Computed Tomography Imaging Indication Using Natural Language Processing for Evaluation of Surveillance Patterns in Long-Term Lung Cancer Survivors.JCO clinical cancer informatics · 2025Article
- The Fine-Tuned Large Language Model for Extracting the Progressive Bone Metastasis from Unstructured Radiology Reports.Journal of imaging informatics in medicine · 2025Article
- Automated classification of brain MRI reports using fine-tuned large language models.Neuroradiology · 2024Article
- Article
- Enhancing the Interpretability of Malaria and Typhoid Diagnosis with Explainable AI and Large Language Models.Tropical medicine and infectious disease · 2024Article
- Comparison of natural language processing algorithms in assessing the importance of head computed tomography reports written in Japanese.Japanese journal of radiology · 2024Article
- Generative Artificial Intelligence for Chest Radiograph Interpretation in the Emergency Department.JAMA network open · 2023Article
- Empirical evaluation of language modeling to ascertain cancer outcomes from clinical text reports.BMC bioinformatics · 2023Article
- Development and External Validation of an Artificial Intelligence Model for Identifying Radiology Reports Containing Recommendations for Additional Imaging.AJR. American journal of roentgenology · 2023Article
- Deep Learning Approach for Negation and Speculation Detection for Automated Important Finding Flagging and Extraction in Radiology Report: Internal Validation and Technique Comparison Study.JMIR medical informatics · 2023Article
- Automatic text classification of actionable radiology reports of tinnitus patients using bidirectional encoder representations from transformer (BERT) and in-domain pre-training (IDPT).BMC medical informatics and decision making · 2022Article
- Impact of a Clinical Text-Based Fall Prediction Model on Preventing Extended Hospital Stays for Elderly Inpatients: Model Development and Performance Evaluation.JMIR medical informatics · 2022Article
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Authors and funding
9 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundIt is essential for radiologists to communicate actionable findings to the referring clinicians reliably. Natural language processing (NLP) has been shown to help identify free-text radiology reports including actionable findings. However, the application of recent deep learning techniques to radiology reports, which can improve the detection performance, has not been thoroughly examined. Moreover, free-text that clinicians input in the ordering form (order information) has seldom been used to identify actionable reports. This study aims to evaluate the benefits of two new approaches: (1) bidirectional encoder representations from transformers (BERT), a recent deep learning architecture in NLP, and (2) using order information in addition to radiology reports.
methodsWe performed a binary classification to distinguish actionable reports (i.e., radiology reports tagged as actionable in actual radiological practice) from non-actionable ones (those without an actionable tag). 90,923 Japanese radiology reports in our hospital were used, of which 788 (0.87%) were actionable. We evaluated four methods, statistical machine learning with logistic regression (LR) and with gradient boosting decision tree (GBDT), and deep learning with a bidirectional long short-term memory (LSTM) model and a publicly available Japanese BERT model. Each method was used with two different inputs, radiology reports alone and pairs of order information and radiology reports. Thus, eight experiments were conducted to examine the performance.
resultsWithout order information, BERT achieved the highest area under the precision-recall curve (AUPRC) of 0.5138, which showed a statistically significant improvement over LR, GBDT, and LSTM, and the highest area under the receiver operating characteristic curve (AUROC) of 0.9516. Simply coupling the order information with the radiology reports slightly increased the AUPRC of BERT but did not lead to a statistically significant improvement. This may be due to the complexity of clinical decisions made by radiologists.
conclusionsBERT was assumed to be useful to detect actionable reports. More sophisticated methods are required to use order information effectively.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.