ArticleRadiology. Artificial intelligence2023
Domain-adapted Large Language Models for Classifying Nuclear Medicine Reports.
Article in Radiology. Artificial intelligence, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
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Who cites it
19 citing papers in PubMed.
- Key Measures for Evaluating Diagnostic Accuracy in Multi-Class Classification: An Overview and Simulation-Based Comparison.Korean journal of radiology · 2026Review
- Empowering liver cancer diagnosis and treatment with foundation models: technological innovation and clinical practice.Clinical and experimental medicine · 2026Review
- Conceptual proposal for LLM-generated FDG PET/CT follow-up reports in melanoma: a pilot study on model stability and blinded expert evaluation.Frontiers in nuclear medicine · 2026Article
- Trends and Trajectories in the Rise of Large Language Models in Radiology: Scoping Review.JMIR medical informatics · 2025Article
- Vision-language model performance on the Japanese Nuclear Medicine Board Examination: high accuracy in text but challenges with image interpretation.Annals of nuclear medicine · 2025Article
- Large Language Models for Translational Cancer Informatics.JCO clinical cancer informatics · 2025Review
- Impact of hospital-specific domain adaptation on BERT-based models to classify neuroradiology reports.European radiology · 2025Article
- Development and evaluation of large-language models (LLMs) for oncology: A scoping review.PLOS digital health · 2025Article
- Large Language Model Architectures in Health Care: Scoping Review of Research Perspectives.Journal of medical Internet research · 2025Article
- Empowering PET imaging reporting with retrieval-augmented large language models and reading reports database: a pilot single center study.European journal of nuclear medicine and molecular imaging · 2025Article
- Assessing large language models for Lugano classification of malignant lymphoma in Japanese FDG-PET reports.EJNMMI reports · 2025Article
- [Advancements in artificial intelligence for the precise diagnosis and treatment of hematological malignancies].Zhonghua xue ye xue za zhi = Zhonghua xueyexue zazhi · 2025Review
- Large Language Models and Large Multimodal Models in Medical Imaging: A Primer for Physicians.Journal of nuclear medicine : official publication, Society of Nuclear Medicine · 2025Review
- Uncertainty-aware automatic TNM staging classification for [BMC medical informatics and decision making · 2024Article
- Review
- Advancing medical imaging with language models: featuring a spotlight on ChatGPT.Physics in medicine and biology · 2024Review
- Personalized Impression Generation for PET Reports Using Large Language Models.Journal of imaging informatics in medicine · 2024Article
- One Hot (Up)Take: Vision, Language, and Domain Knowledge in PET/CT Reporting.Radiology. Artificial intelligence · 2023Article
- Automatic Personalized Impression Generation for PET Reports Using Large Language Models.ArXiv · 2023Article
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5 authors.
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Abstract
Purpose: To evaluate the impact of domain adaptation on the performance of language models in predicting five-point Deauville scores on the basis of clinical fluorine 18 fluorodeoxyglucose PET/CT reports. Materials and Methods: The authors retrospectively retrieved 4542 text reports and images for fluorodeoxyglucose PET/CT lymphoma examinations from 2008 to 2018 in the University of Wisconsin-Madison institutional clinical imaging database. Of these total reports, 1664 had Deauville scores that were extracted from the reports and served as training labels. The bidirectional encoder representations from transformers (BERT) model and initialized BERT models BioClinicalBERT, RadBERT, and RoBERTa were adapted to the nuclear medicine domain by pretraining using masked language modeling. These domain-adapted models were then compared with the non-domain-adapted versions on the task of five-point Deauville score prediction. The language models were compared against vision models, multimodal vision-language models, and a nuclear medicine physician, with sevenfold Monte Carlo cross-validation. Means and SDs for accuracy are reported, with Results: Domain adaptation improved the performance of all language models ( Conclusion: Domain adaptation improved the performance of large language models in predicting Deauville scores in PET/CT reports.
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