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Related Experiment Video

Updated: Jul 24, 2025

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

Will code one day run a code? Performance of language models on ACEM primary examinations and implications.

Jesse Smith¹, Philip Mc Choi^2,3, Paul Buntine^1,2

¹Eastern Health Emergency Medicine Program, Eastern Health, Melbourne, Victoria, Australia.

Emergency Medicine Australasia : EMA

|July 6, 2023

Summary

Large language models (LLMs) passed the Australasian College for Emergency Medicine primary examination, showing promise for medical education. GPT 4.0 notably exceeded average candidate performance in this emergency medicine assessment.

Keywords:

Bing artificial intelligence chat GPT emergency medicine medical education specialty examination

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Area of Science:

Artificial Intelligence in Medicine
Medical Education Technology
Emergency Medicine Assessment

Background:

Large language models (LLMs) exhibit variable performance on specialist medical examinations.
The efficacy of LLMs in emergency medicine assessments is not well-established.

Purpose of the Study:

To evaluate the performance of prominent LLMs on the Australasian College for Emergency Medicine (ACEM) primary examination.
To determine the potential utility of LLMs in emergency medicine training and evaluation.

Main Methods:

Three leading LLMs (OpenAI's GPT series, Google's Bard, Microsoft's Bing Chat) were tested.
Performance was assessed using a practice ACEM primary examination.

Main Results:

All evaluated LLMs successfully passed the ACEM primary examination.
GPT 4.0 demonstrated superior performance compared to the average human candidate.

Conclusions:

LLMs show potential as valuable tools for medical education and practice in emergency medicine.
Despite passing the examination, existing limitations of LLMs require further investigation and discussion.