聊天GPT与HuggingChat在产科医生和妇科医生主题上的表现
Gabrielle Kirshteyn1, Roei Golan2, Mark Chaet3
1Obstetrics and Gynecology, Florida State University College of Medicine, Tallahassee, USA.
Cureus
|April 15, 2024
概括
像ChatGPT和HuggingChat这样的大型语言模型在产科和妇科 (OB-GYN) 医学问题上显示出希望. 这项研究发现,在产妇科医生考试中,这些AI工具之间没有显著的性能差异.
科学领域:
- 医疗人工智能 医疗人工智能
- 医疗教育 技术 技术 医学教育
背景情况:
- 大型语言模型 (LLM) 在医学中具有潜力,但它们在产科和妇科 (OB-GYN) 的可靠性尚未得到充分理解.
- 评估LLM绩效对于安全有效地融入医疗实践至关重要.
研究的目的:
- 测量和比较ChatGPT和HuggingChat在产科医生医学检查问题上的表现.
- 提供关于这些人工智能工具在专业医疗领域的有效性的见解.
主要方法:
- 聊天GPT和拥抱聊天使用两个标准化的多选题库进行了测试:国家医学检查委员会 (NBME) 和妇科和产科教授协会 (APGO) uWISE.
- 分析了答案的正确性,并使用双比例z测试统计评估了绩效差异.
主要成果:
- 在任何一项检查中,在ChatGPT和HuggingChat之间都没有发现统计学上显著的性能差异.
- 在测试1 (NBME) 中,ChatGPT的准确率达到90%,在测试2 (APGO uWISE) 中达到70%.
- 拥抱聊天在测试1中达到85%的准确率,在测试2中达到62%的准确率.
结论:
- 聊天GPT和拥抱聊天都显示出医疗应用的巨大潜力,特别是在产妇科.
- 了解人工智能工具的功能和局限性对于它们在医疗保健和医学教育中的适当使用至关重要.
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