Fine-Tuning, Retrieval-Augmented Generation, and Hybrid Large Language Models for Postoperative Decision Support: Comparative Analysis.

Srinivasagam Prabha¹, Bernardo Gabriele Collaco¹, Cesar Abraham Gomez-Cabello¹, Syed Ali Haider¹, Ariana Genovese¹, Zhihui Fang², Nadia Wood³, Sanjay Bagaria⁴, Cui Tao⁵, Antonio Jorge Forte^1,5,6

¹Division of Plastic Surgery, Mayo Clinic in Florida, 4500 San Pablo Road South, Jacksonville, FL, 32224, United States, 1 904-953-2000.

²Division of Clinical Trials and Biostatistics, Mayo Clinic in Florida, Jacksonville, FL, United States.

³Department of Radiology AI IT, Mayo Clinic, Rochester, MN, United States.

⁴Department of Surgery, Mayo Clinic in Florida, Jacksonville, FL, United States.

⁵Department of Artificial Intelligence and Informatics, Mayo Clinic in Florida, Jacksonville, FL, United States.

⁶Center for Digital Health, Mayo Clinic, Rochester, MN, United States.

PubMed DOI

Abstract

BACKGROUND: Large language models (LLMs) show growing potential for decision support. However, integrating domain-specific medical knowledge while maintaining accuracy, safety, and interpretability remains challenging for postoperative discharge instructions and patient education. Fine-tuning, retrieval-augmented generation (RAG), and hybrid fine-tuning+RAG approaches are prominent strategies for…

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