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Related Experiment Video

Updated: Jul 16, 2026

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Published on: December 6, 2024

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

Srinivasagam Prabha¹, Bernardo Gabriele Collaco¹, Cesar Abraham Gomez-Cabello¹

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

Journal of Medical Internet Research

|July 15, 2026

Summary

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Integrating medical knowledge into large language models (LLMs) significantly improves postoperative decision support. The hybrid fine-tuning and retrieval-augmented generation (RAG) approach demonstrated the highest accuracy and safety for patient education.

Area of Science:

Artificial Intelligence in Medicine
Clinical Decision Support Systems
Natural Language Processing

Background:

Large language models (LLMs) offer potential for clinical decision support but face challenges in integrating medical knowledge for accuracy and safety.
Existing methods like fine-tuning and retrieval-augmented generation (RAG) have not been systematically compared for postoperative care.

Purpose of the Study:

To compare the performance, reliability, and safety of baseline, fine-tuning, RAG, and hybrid fine-tuning+RAG LLM configurations.
To evaluate LLM effectiveness for postoperative decision support, including discharge instructions and patient education.

Main Methods:

A comparative evaluation of four LLM configurations (baseline, fine-tuning, RAG, fine-tuning+RAG) using Google Gemini 2.5 Flash.

Keywords:

AI decision support fine-tuning large language models postoperative care retrieval-augmented generation

Related Experiment Videos

Last Updated: Jul 16, 2026

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Published on: December 6, 2024

Model adaptation and validation using 600 postoperative Q&A pairs, with final evaluation on 150 diverse queries.

Independent assessment by 3 clinical experts for accuracy, safety, completeness, and relevance, alongside automated metrics for readability and hallucination.

Main Results:

All knowledge-enhanced LLMs significantly outperformed the baseline model in overall accuracy (e.g., fine-tuning+RAG achieved 97.3%).
The fine-tuning+RAG configuration showed the highest clinical accuracy (96.7%) for in-scope queries and superior classification performance (100% precision, 96.7% recall).
Knowledge-enhanced models improved safety/refusal accuracy, though readability was slightly reduced, partly due to standardized safety information.

Conclusions:

Incorporating domain-specific knowledge via fine-tuning, RAG, or hybrid approaches enhances LLM performance for postoperative decision support.
The fine-tuning+RAG model demonstrated the most favorable outcomes, suggesting its promise for patient education and clinical guidance.
Further validation, readability optimization, and human oversight are crucial before patient-facing deployment of these enhanced LLMs.