Grounded large language models for diagnostic prediction in real-world emergency department settings.

ABSTRACT:

Objective

To evaluate predictive diagnostic performance of open- and closed-source large language models (LLMs) in emergency medicine, addressing the urgent need for innovative clinical decision support tools amid rising patient volumes and staffing shortages.

Materials and methods

We generated 2370 AI-driven diagnostic predictions (Top-5 diagnoses from each of 6 model pipelines per patient), using data from 79 real-world emergency department cases collected consecutively during a 24-hour peak influx period at a tertiary care center. Pipelines combined open- and closed-source embedding models (text-embedding-ada-002, MXBAI) with foundational models (GPT-4, Llama3, and Qwen2) grounded via retrieval-augmented generation using emergency medicine textbooks. Models' predictions w

SUBMITTER: Niset A 

PROVIDER: S-EPMC12539180 | biostudies-literature | 2025 Oct

REPOSITORIES: biostudies-literature