ArticleNeuroradiology2024
Automated classification of brain MRI reports using fine-tuned large language models.
Article in Neuroradiology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.
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Who cites it
22 citing papers in PubMed.
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- Fine-Tuned Large Language Model for Extracting Pretreatment Pancreatic Cancer According to Computed Tomography Radiology Reports.Journal of imaging informatics in medicine · 2026Article
- Automated RECIST tumor response classification through prompt-guided large language models.Scientific reports · 2026Article
- Context-Aware Sentence Classification of Radiology Reports Using Synthetic Data: Development and Validation Study.Journal of medical Internet research · 2026Article
- ModernBERT is more efficient than conventional BERT for chest CT findings classification in Japanese radiology reports.Scientific reports · 2026Article
- Leveraging Large Language Models for Accurate AO Fracture Classification from CT Text Reports.Journal of imaging informatics in medicine · 2026Article
- Large language models for bioinformatics.Quantitative biology (Beijing, China) · 2026Review
- From diagnostics to education: Multi-domain evaluation of LLM chatbots in neurology.Journal of Taibah University Medical Sciences · 2026Review
- Efficacy of a large language model in classifying branch-duct intraductal papillary mucinous neoplasms.Abdominal radiology (New York) · 2026Article
- Pituitary neuroendocrine tumor: evaluation with super resolution deep learning reconstruction : Research.Neuroradiology · 2026Article
- Digital twin-supported behavioral intention in mothers of young children to prevent childhood obesity: a large language model-based intervention study.Frontiers in artificial intelligence · 2026Article
- LLM-powered TNM staging of neuroendocrine tumors from PET/CT reports.BMC medical imaging · 2025Article
- Trends and Trajectories in the Rise of Large Language Models in Radiology: Scoping Review.JMIR medical informatics · 2025Article
- Efficacy of Fine-Tuned Large Language Model in CT Protocol Assignment as Clinical Decision-Supporting System.Journal of imaging informatics in medicine · 2025Article
- Large language models versus classical machine learning performance in COVID-19 mortality prediction using high-dimensional tabular data.Scientific reports · 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
- The Fine-Tuned Large Language Model for Extracting the Progressive Bone Metastasis from Unstructured Radiology Reports.Journal of imaging informatics in medicine · 2025Article
- Assessing large language models for Lugano classification of malignant lymphoma in Japanese FDG-PET reports.EJNMMI reports · 2025Article
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Abstract
purposeThis study aimed to investigate the efficacy of fine-tuned large language models (LLM) in classifying brain MRI reports into pretreatment, posttreatment, and nontumor cases.
methodsThis retrospective study included 759, 284, and 164 brain MRI reports for training, validation, and test dataset. Radiologists stratified the reports into three groups: nontumor (group 1), posttreatment tumor (group 2), and pretreatment tumor (group 3) cases. A pretrained Bidirectional Encoder Representations from Transformers Japanese model was fine-tuned using the training dataset and evaluated on the validation dataset. The model which demonstrated the highest accuracy on the validation dataset was selected as the final model. Two additional radiologists were involved in classifying reports in the test datasets for the three groups. The model's performance on test dataset was compared to that of two radiologists.
resultsThe fine-tuned LLM attained an overall accuracy of 0.970 (95% CI: 0.930-0.990). The model's sensitivity for group 1/2/3 was 1.000/0.864/0.978. The model's specificity for group1/2/3 was 0.991/0.993/0.958. No statistically significant differences were found in terms of accuracy, sensitivity, and specificity between the LLM and human readers (p ≥ 0.371). The LLM completed the classification task approximately 20-26-fold faster than the radiologists. The area under the receiver operating characteristic curve for discriminating groups 2 and 3 from group 1 was 0.994 (95% CI: 0.982-1.000) and for discriminating group 3 from groups 1 and 2 was 0.992 (95% CI: 0.982-1.000).
conclusionFine-tuned LLM demonstrated a comparable performance with radiologists in classifying brain MRI reports, while requiring substantially less time.
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