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Reply to the Letter to the Editor: Improving generative-AI performance in radiology through test-time compute.

作者信息

Nowak Sebastian, Salam Babak, Luetkens Julian A, Isaak Alexander

机构信息

Department of Diagnostic and Interventional Radiology, University Hospital Bonn, Bonn, Germany.

Quantitative Imaging Lab Bonn (QILaB), University Hospital Bonn, Bonn, Germany.

出版信息

Eur Radiol. 2025 May 6. doi: 10.1007/s00330-025-11665-3.

DOI:10.1007/s00330-025-11665-3
PMID:40328955
Abstract
摘要

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本文引用的文献

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Large language models for error detection in radiology reports: a comparative analysis between closed-source and privacy-compliant open-source models.用于放射学报告错误检测的大语言模型：闭源模型与符合隐私规定的开源模型的对比分析
Eur Radiol. 2025 Feb 20. doi: 10.1007/s00330-025-11438-y.
2
Privacy-ensuring Open-weights Large Language Models Are Competitive with Closed-weights GPT-4o in Extracting Chest Radiography Findings from Free-Text Reports.在从自由文本报告中提取胸部X光检查结果方面，确保隐私的开放权重大型语言模型与封闭权重的GPT-4o具有竞争力。
Radiology. 2025 Jan;314(1):e240895. doi: 10.1148/radiol.240895.
3
AI models collapse when trained on recursively generated data.
当在递归生成的数据上训练 AI 模型时，模型会崩溃。
Nature. 2024 Jul;631(8022):755-759. doi: 10.1038/s41586-024-07566-y. Epub 2024 Jul 24.
4
Generative Pre-trained Transformer 4 makes cardiovascular magnetic resonance reports easy to understand.生成式预训练转换器 4 使得心血管磁共振报告易于理解。
J Cardiovasc Magn Reson. 2024 Summer;26(1):101035. doi: 10.1016/j.jocmr.2024.101035. Epub 2024 Mar 7.