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

IF 12.4 1区 医学 Q1 HEALTH CARE SCIENCES & SERVICES NPJ Digital Medicine Pub Date : 2024-12-18 DOI:10.1038/s41746-024-01391-3
Chin Siang Ong, Nicholas T. Obey, Yanan Zheng, Arman Cohan, Eric B. Schneider
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Abstract

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.

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SurgeryLLM 是一个使用检索增强生成技术的大型语言模型框架,当病人的特定数据呈现在它面前时,它能从当前循证外科指南中获取特定领域的知识。基于指南的信息的成功整合标志着在提高外科医生效率、改善患者安全和优化手术效果方面迈出了实质性的一步。
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来源期刊
CiteScore
25.10
自引率
3.30%
发文量
170
审稿时长
15 weeks
期刊介绍: npj Digital Medicine is an online open-access journal that focuses on publishing peer-reviewed research in the field of digital medicine. The journal covers various aspects of digital medicine, including the application and implementation of digital and mobile technologies in clinical settings, virtual healthcare, and the use of artificial intelligence and informatics. The primary goal of the journal is to support innovation and the advancement of healthcare through the integration of new digital and mobile technologies. When determining if a manuscript is suitable for publication, the journal considers four important criteria: novelty, clinical relevance, scientific rigor, and digital innovation.
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