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Related Experiment Video

Updated: May 2, 2026

Pedicle Screw Placement Using an Augmented Reality Head-Mounted Display in a Porcine Model

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Published on: May 24, 2024

Explainable and evidence-linked recommendations for spine surgery via a retrieval-augmented LLM agent.

Yiren Li¹, Mingyu Lv¹, Liang Cheng¹

¹Department of Spine Surgery, Center for Orthopedic Surgery, The Third Affiliated Hospital, Southern Medical University, Guangzhou, China.

European Journal of Radiology

|February 21, 2026

Summary

Gemini 2.5 Flash and DeepSeek show superior performance in spine disease decision support using a novel RAG framework. This study highlights the potential of domain-adapted Large Language Models (LLMs) for enhancing clinical care.

Keywords:

Clinical decision support Expert evaluation framework Large Language Models (LLMs)Retrieval-Augmented Generation (RAG)Spinal disorders

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Published on: June 13, 2025

Area of Science:

Artificial Intelligence in Medicine
Spine Surgery Decision Support
Clinical Informatics

Background:

Clinical decision-making for spinal diseases requires integrating imaging, neurological status, bone integrity, and patient goals.
Large Language Models (LLMs) offer potential for intelligent decision support but need rigorous evaluation for reliability and interpretability.

Purpose of the Study:

To propose and evaluate a novel retrieval augmented generation (RAG)-based framework for spinal disease decision support.
To systematically compare the performance of four advanced LLM agents (Gemini 2.5 Flash, DeepSeek, GPT-4o, GPT-4o-mini) within this framework.

Main Methods:

A RAG framework was developed, incorporating domain-specific prompting, structured responses, and evidence tracking.
200 real-world spinal cases were used to assess diagnostic, therapeutic, and follow-up tasks.
Five spine surgeons evaluated LLM outputs using an 11-dimension rubric, with statistical analysis of inter-group differences.

Main Results:

Gemini 2.5 Flash achieved the highest overall score, significantly outperforming other models.
DeepSeek demonstrated superior performance in differential diagnosis completeness.
Substantial inter-model variability was observed in spine-specific clinical reasoning.

Conclusions:

Gemini 2.5 Flash and DeepSeek exhibited superior clinical accuracy, comprehensiveness, and usability for spine decision support.
Domain-adapted RAG agents show promise for enhancing evidence-based spinal care.
Future work should explore multimodal data integration and prospective clinical validation.