Scholarlink

Artificial intelligence is gaining attraction in more ways than ever before. The popularity of language models and AI-based businesses has soared since ChatGPT was made available to the public via the OpenAI web platform. It gains popularity in a very short period because of its real-world problem-solving capability. Considering the widespread use of ChatGPT and the people relying on it, this study determined how reliable ChatGPT can be used for learning in the medical domain. The capability of ChatGPT was evaluated using the questions of Harvard University gross anatomy and the United States Medical Licensing Examination (USMLE). The outcome of the ChatGPT was analyzed using a 2-way ANOVA and post-hoc analysis. Both tests showed systematic covariation between format and prompt. Furthermore, the physician adjudicators independently rated the outcome’s accuracy, concordance, and insight into the answers given by ChatGPT. As a result of the analysis, ChatGPT-generated answers were more context-oriented and represented a better model for deductive reasoning than regular Google search results. Furthermore, ChatGPT obtained 58.8% on logical questions and 60% on ethical questions. This means that the ChatGPT is approaching the passing range for logical questions and has crossed the threshold for ethical questions. These results indicate that ChatGPT and other language-learning models can be invaluable tools for e-learners.

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Unlocking the Potential of Large Language Models for AI-Assisted Medical Education: A Case Study with ChatGPT

  • 会议论文

  • 发表日期：

    2025年

查看全文
  • Prabin Sharma,
  • Kisan Thapa,
  • Prastab Dhakal,
  • Mala Deep Upadhaya,
  • Dikshya Thapa,
  • Santosh Adhikari,
  • Salik Ram Khanal,
  • Vitor Filipe

摘要

Artificial intelligence is gaining attraction in more ways than ever before. The popularity of language models and AI-based businesses has soared since ChatGPT was made available to the public via the OpenAI web platform. It gains popularity in a very short period because of its real-world problem-solving capability. Considering the widespread use of ChatGPT and the people relying on it, this study determined how reliable ChatGPT can be used for learning in the medical domain. The capability of ChatGPT was evaluated using the questions of Harvard University gross anatomy and the United States Medical Licensing Examination (USMLE). The outcome of the ChatGPT was analyzed using a 2-way ANOVA and post-hoc analysis. Both tests showed systematic covariation between format and prompt. Furthermore, the physician adjudicators independently rated the outcome’s accuracy, concordance, and insight into the answers given by ChatGPT. As a result of the analysis, ChatGPT-generated answers were more context-oriented and represented a better model for deductive reasoning than regular Google search results. Furthermore, ChatGPT obtained 58.8% on logical questions and 60% on ethical questions. This means that the ChatGPT is approaching the passing range for logical questions and has crossed the threshold for ethical questions. These results indicate that ChatGPT and other language-learning models can be invaluable tools for e-learners.