一项基于临床医生的大型语言模型在回答医学问题的比较研究:喘病例
Yong Yin1,2,3,4, Mei Zeng2, Hansong Wang5
1Department of Respiratory Medicine, Hainan Branch, Shanghai Children's Medical Center, School of Medicine, Shanghai Jiao Tong University, Sanya, China.
Frontiers in pediatrics
|May 12, 2025
概括
与YouChat和Perplexity相比,GPT-4.0和GPT-3.5在回答儿科喘问题方面表现优越. 临床医生应该批判性地评估人工智能生成的医疗信息.
科学领域:
- 人工智能在医学中的应用
- 临床信息学 临床信息学
- 儿科肺病学 儿科肺病学
背景情况:
- 大型语言模型 (LLM) 越来越多地用于医疗信息检索.
- 在临床实践中评估LLM的准确性和实用性至关重要.
研究的目的:
- 为了比较四个主要LLM (GPT-3.5,GPT-4.0,YouChat,Perplexity) 在回答儿科喘相关问题的表现.
- 评估临床医生对LLM产生的反应的看法.
主要方法:
- 75名临床医生评估了LLM对32个常见的儿科喘问题的反应.
- 评估的重点是准确性,正确性,完整性和实用性.
- 绩效分数被分配给每一个LLM.
主要成果:
- 在所有评估的维度中,GPT-4.0表现出最高的性能.
- 聊展示了最低的表现.
- GPT-3.5和GPT-4.0的表现优于YouChat和Perplexity,GPT-3.5和GPT-4.0.0之间没有显著差异.
结论:
- LLM 可以提供合理准确和完整的医疗答案.
- 临床医生必须批判性地评估人工智能生成的内容并验证信息.
- 未来的进展可能会将LLM定位为医疗专业人员的可靠工具.
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