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Will code one day run a code? Performance of language models on ACEM primary examinations and implications
Jesse Smith1, Philip Mc Choi2,3, Paul Buntine1,2
1Eastern Health Emergency Medicine Program, Eastern Health, Melbourne, Victoria, Australia.
Objective:
Large language models (LLMs) have demonstrated mixed results in their ability to pass various specialist medical examination and their performance within the field of emergency medicine remains unknown.
Methods:
We explored the performance of three prevalent LLMs (OpenAI's GPT series, Google's Bard, and Microsoft's Bing Chat) on a practice ACEM primary examination.
Results:
All LLMs achieved a passing score, with scores with GPT 4.0 outperforming the average candidate.
Conclusion:
Large language models, by passing the ACEM primary examination, show potential as tools for medical education and practice. However, limitations exist and are discussed.
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