Fine-Tuning, Retrieval-Augmented Generation, and Hybrid Large Language Models for Postoperative Decision Support: Comparative Analysis
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 knowledge integration, but their comparative performance in postoperative care has not been systemat
Record details
Published: 14 July 2026
Source: JMIR (Journal of Medical Internet Research)
Category: Research
Topics: Safety & alignment · Healthcare · Children & education
Retrieved: 14 July 2026
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ethics.ai (14 July 2026), “Fine-Tuning, Retrieval-Augmented Generation, and Hybrid Large Language Models for Postoperative Decision Support: Comparative Analysis,” evidence record 2426, https://ethics.ai/record/2426 (originally published by JMIR (Journal of Medical Internet Research)).
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