SurgeryLLM: a retrieval-augmented generation large language model framework for surgical decision support and workflow enhancement

被引：0
|
作者：
Ong, Chin Siang [1 ,2 ]
Obey, Nicholas T. [1 ]
Zheng, Yanan [3 ]
Cohan, Arman [3 ,4 ]
Schneider, Eric B. [1 ]
机构：
[1] Department of Surgery, Yale School of Medicine, New Haven,CT, United States
[2] Harvard T.H. Chan School of Public Health, Boston,MA, United States
[3] Department of Computer Science, Yale University, New Haven,CT, United States
[4] Wu Tsai Institute, Yale University, New Haven,CT, United States
来源：
npj Digital Medicine | 2024年 / 7卷 / 01期
关键词：
Modeling languages;
D O I：
10.1038/s41746-024-01391-3
中图分类号：
学科分类号：
摘要：
SurgeryLLM, a large language model framework using Retrieval Augmented Generation demonstrably incorporated domain-specific knowledge from current evidence-based surgical guidelines when presented with patient-specific data. The successful incorporation of guideline-based information represents a substantial step toward enabling greater surgeon efficiency, improving patient safety, and optimizing surgical outcomes. © The Author(s) 2024.
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