SurgeryLLM: a retrieval-augmented generation large language model framework for surgical decision support and workflow enhancement
Chin Siang Ong, Nicholas T. Obey, Yanan Zheng, Arman Cohan, Eric B. Schneider
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.
Topics & Concepts
WorkflowComputer scienceGuidelineDomain (mathematical analysis)Decision support systemMedicineArtificial intelligenceDatabasePathologyMathematical analysisMathematicsColorectal Cancer Screening and DetectionRadiomics and Machine Learning in Medical ImagingCardiac, Anesthesia and Surgical Outcomes