ArticleRadiology. Artificial intelligence2022
Performance of Multiple Pretrained BERT Models to Automate and Accelerate Data Annotation for Large Datasets.
Article in Radiology. Artificial intelligence, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers.
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Who cites it
23 citing papers in PubMed.
- What Users Say About Reimbursable Digital Therapeutics in Germany: Large-Scale App Store Review Analysis Using a Large Language Model.JMIR human factors · 2026Article
- Artificial intelligence for chest radiography: an overview of techniques, challenges, and future directions.npj health systems · 2026Review
- Classification of health product defect reports by deep learning.Scientific reports · 2026Article
- High-Performance Prompting for LLM Extraction of Compression Fracture Findings from Radiology Reports.Journal of imaging informatics in medicine · 2026Article
- Semantic Explanation for Malaria Diagnosis: Comparing Human and Machine Generated Annotations forIEEE open journal of engineering in medicine and biology · 2026Article
- Impact of hospital-specific domain adaptation on BERT-based models to classify neuroradiology reports.European radiology · 2025Article
- The Evolution of Radiology Image Annotation in the Era of Large Language Models.Radiology. Artificial intelligence · 2025Review
- In-Context Learning with Large Language Models: A Simple and Effective Approach to Improve Radiology Report Labeling.Healthcare informatics research · 2025Article
- From Revisions to Insights: Converting Radiology Report Revisions into Actionable Educational Feedback Using Generative AI Models.Journal of imaging informatics in medicine · 2025Article
- Ontology-guided machine learning outperforms zero-shot foundation models for cardiac ultrasound text reports.Scientific reports · 2025Article
- Text intelligent correction in English translation: A study on integrating models with dependency attention mechanism.PloS one · 2025Article
- A scoping review of large language model based approaches for information extraction from radiology reports.NPJ digital medicine · 2024Article
- A New Era of Text Mining in Radiology with Privacy-Preserving LLMs.Radiology. Artificial intelligence · 2024Article
- Evaluating the performance of Generative Pre-trained Transformer-4 (GPT-4) in standardizing radiology reports.European radiology · 2024Article
- Artificial Intelligence-Assisted Cancer Status Detection in Radiology Reports.Cancer research communications · 2024Article
- From intuition to AI: evolution of small molecule representations in drug discovery.Briefings in bioinformatics · 2023Article
- Natural language processing to predict isocitrate dehydrogenase genotype in diffuse glioma using MR radiology reports.European radiology · 2023Article
- To BERT or not to BERT: advancing non-invasive prediction of tumor biomarkers using transformer-based natural language processing (NLP).European radiology · 2023Article
- Domain-adapted Large Language Models for Classifying Nuclear Medicine Reports.Radiology. Artificial intelligence · 2023Article
- Feasibility of Using the Privacy-preserving Large Language Model Vicuna for Labeling Radiology Reports.Radiology · 2023Article
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6 authors.
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Abstract
Purpose: To develop and evaluate domain-specific and pretrained bidirectional encoder representations from transformers (BERT) models in a transfer learning task on varying training dataset sizes to annotate a larger overall dataset. Materials and Methods: The authors retrospectively reviewed 69 095 anonymized adult chest radiograph reports (reports dated April 2020-March 2021). From the overall cohort, 1004 reports were randomly selected and labeled for the presence or absence of each of the following devices: endotracheal tube (ETT), enterogastric tube (NGT, or Dobhoff tube), central venous catheter (CVC), and Swan-Ganz catheter (SGC). Pretrained transformer models (BERT, PubMedBERT, DistilBERT, RoBERTa, and DeBERTa) were trained, validated, and tested on 60%, 20%, and 20%, respectively, of these reports through fivefold cross-validation. Additional training involved varying dataset sizes with 5%, 10%, 15%, 20%, and 40% of the 1004 reports. The best-performing epochs were used to assess area under the receiver operating characteristic curve (AUC) and determine run time on the overall dataset. Results: The highest average AUCs from fivefold cross-validation were 0.996 for ETT (RoBERTa), 0.994 for NGT (RoBERTa), 0.991 for CVC (PubMedBERT), and 0.98 for SGC (PubMedBERT). DeBERTa demonstrated the highest AUC for each support device trained on 5% of the training set. PubMedBERT showed a higher AUC with a decreasing training set size compared with BERT. Training and validation time was shortest for DistilBERT at 3 minutes 39 seconds on the annotated cohort. Conclusion: Pretrained and domain-specific transformer models required small training datasets and short training times to create a highly accurate final model that expedites autonomous annotation of large datasets.
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