Work

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

Year: 2024

Type: article

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.

Source: npj Digital Medicine

DOI: 10.1038/s41746-024-01391-3

Authors: Chin Siang Ong, Nicholas T. Obey, Yanan Zheng, Arman Cohan, Eric B. Schneider

Institutions: Harvard University, Yale University

Language: English

Topic: Artificial Intelligence in Healthcare and Education

Subfield: Health Informatics

Field: Medicine

Domain: Health Sciences

SDG: Good health and well-being

Open Access status: gold

Estimated APC paid: $3,790