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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