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

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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Publication Details

Journal
npj Digital Medicine
Published
2024-12-18
DOI
https://doi.org/10.1038/s41746-024-01391-3
Citations
23
Primary Topic
Colorectal Cancer Screening and Detection
Type
article
Field-Weighted Citation Impact
6.50
Controls
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article

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

Yanan Zheng, Eric B. Schneider, Arman Cohan, Chin Siang Ong et al.
23 citations
npj Digital Medicine
Colorectal Cancer Screening and Detection
6.50
article

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

Yanan Zheng, Eric B. Schneider, Arman Cohan, Chin Siang Ong, Nicholas T. Obey
article en
23 citations

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.

npj Digital MedicineVol. 7(1)
Harvard University (US), Yale University (US)
Peace, Justice and strong institutions
Openalex Percentile: Top 2%
Colorectal Cancer Screening and Detection
6.50
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