Heydari Kimia, Enichen Elizabeth J, Li Ben, Kvedar Joseph C
Harvard Medical School, Boston, MA, USA.
Division of Vascular Surgery, University of Toronto, Toronto, ON, Canada.
NPJ Digit Med. 2025 Aug 18;8(1):529. doi: 10.1038/s41746-025-01941-3.
Integrating large language models (LLMs) into oncology holds promise for clinical decision support. Woollie is an LLM recently developed by Zhu et al., fine-tuned using radiology impression notes from Memorial Sloan Kettering Cancer Center and externally validated on UCSF oncology datasets. This methodology prioritizes data accuracy, preempts catastrophic forgetting, and demonstrates unparalleled rigor in predicting the progression of various cancer types. This work establishes a foundation for reliable, scalable, and equitable applications of LLMs in oncology.
将大语言模型（LLMs）整合到肿瘤学中有望为临床决策提供支持。Woollie是朱等人最近开发的一个大语言模型，它使用纪念斯隆凯特琳癌症中心的放射学印象记录进行了微调，并在加州大学旧金山分校肿瘤学数据集上进行了外部验证。这种方法优先考虑数据准确性，避免灾难性遗忘，并在预测各种癌症类型的进展方面展现出无与伦比的严谨性。这项工作为大语言模型在肿瘤学中的可靠、可扩展和公平应用奠定了基础。
