Tordjman Mickael, Bolger Ian, Yuce Murat, Restrepo Francisco, Liu Zelong, Dercle Laurent, McGale Jeremy, Meribout Anis L, Liu Mira M, Beddok Arnaud, Lee Hao-Chih, Rohren Scott, Yu Ryan, Mei Xueyan, Taouli Bachir
Biomedical Engineering & Imaging Institute, Mount Sinai Health System, New York, NY 10029, USA.
Department of Diagnostic, Molecular and Interventional Radiology, Mount Sinai Health System, New York, NY 10029, USA.
J Clin Med. 2025 May 8;14(10):3285. doi: 10.3390/jcm14103285.
Recently, there has been tremendous interest on the use of large language models (LLMs) in radiology. LLMs have been employed for various applications in cancer imaging, including improving reporting speed and accuracy via generation of standardized reports, automating the classification and staging of abnormal findings in reports, incorporating appropriate guidelines, and calculating individualized risk scores. Another use of LLMs is their ability to improve patient comprehension of imaging reports with simplification of the medical terms and possible translations to multiple languages. Additional future applications of LLMs include multidisciplinary tumor board standardizations, aiding patient management, and preventing and predicting adverse events (contrast allergies, MRI contraindications) and cancer imaging research. However, limitations such as hallucinations and variable performances could present obstacles to widespread clinical implementation. Herein, we present a review of the current and future applications of LLMs in cancer imaging, as well as pitfalls and limitations.
最近，人们对大语言模型（LLMs）在放射学中的应用产生了极大兴趣。大语言模型已被用于癌症成像的各种应用中，包括通过生成标准化报告来提高报告速度和准确性、自动对报告中的异常发现进行分类和分期、纳入适当的指南以及计算个性化风险评分。大语言模型的另一个用途是能够通过简化医学术语并可能翻译成多种语言来提高患者对影像报告的理解。大语言模型未来的其他应用包括多学科肿瘤委员会标准化、辅助患者管理以及预防和预测不良事件（造影剂过敏、MRI禁忌证）和癌症成像研究。然而，诸如幻觉和性能多变等局限性可能会成为广泛临床应用的障碍。在此，我们对大语言模型在癌症成像中的当前和未来应用以及陷阱和局限性进行综述。
