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.
Authors
- Yanan Zheng (ORCID: https://orcid.org/0000-0002-9823-0191)
- Eric B. Schneider (ORCID: https://orcid.org/0000-0001-7682-0126)
- Arman Cohan (ORCID: https://orcid.org/0000-0002-8954-2724)
- Chin Siang Ong (ORCID: https://orcid.org/0000-0002-4521-0971)
- Nicholas T. Obey
Institutions
- Harvard University (US)
- Yale University (US)
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