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Children master language quickly and with relative ease, supported by both biological predisposition and reinforcement. B. F. Skinner (1957) proposed that language is learned through reinforcement, while Noam Chomsky (1965) argued that language acquisition mechanisms are biologically determined.
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Related Experiment Video

Updated: Jun 4, 2025

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
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Embracing Large Language Models for Adult Life Support Learning.

Serena Patel1, Rohit Patel2

  • 1General Surgery, Imperial College NHS Trust, Ilford, GBR.

Cureus
|December 19, 2024
PubMed
Summary

Large language models (LLMs) like ChatGPT and Bard show similar performance in answering Intermediate Life Support multiple-choice questions. While useful, neither AI model answered all questions accurately, indicating a need for further development in medical education applications.

Keywords:
advanced life supportbardbasic life support blschatgptgoogle bardlarge language models (llm)resus

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Area of Science:

  • Medical Education
  • Artificial Intelligence
  • Clinical Practice

Background:

  • Large language models (LLMs) show potential in aiding medical education by explaining answers to multiple-choice questions (MCQs).
  • Evaluating the efficacy of LLM chatbots in medical assessments is crucial for understanding their utility.

Purpose of the Study:

  • To assess the accuracy and explanation quality of ChatGPT and Bard in answering Intermediate Life Support MCQs.
  • To compare the performance of these LLM chatbots on a test developed by the Resuscitation Council UK.

Main Methods:

  • ChatGPT-3.5 and Bard were tested on ten Intermediate Life Support MCQs.
  • AI responses were scored for accuracy (out of 40 and 10).
  • Explanations were evaluated by three physicians using a 0-3 rating scale; Fleiss kappa assessed inter-rater reliability.

Main Results:

  • Bard and ChatGPT showed similar performance in overall question scoring (p=0.37) and sub-question scoring (p=0.26).
  • The quality of explanations provided by both LLMs was comparable.
  • Both chatbots offered useful correct information, even when answering some questions incorrectly.

Conclusions:

  • ChatGPT and Bard demonstrate similar capabilities in answering Intermediate Life Support MCQs.
  • Neither LLM achieved perfect accuracy, highlighting ongoing development needs for AI in medical education.
  • Further research is needed to optimize LLMs for medical training and assessment.