一个多学科评估的ChatGPT的知识的氨基粉症:观察性研究研究
Ryan C King1, Jamil S Samaan2, Yee Hui Yeo2
1Division of Cardiology, Department of Medicine, University of California, Irvine Medical Center, Orange, CA, United States.
JMIR cardio
|April 19, 2024
概括
像ChatGPT这样的大型语言模型在提供准确的粉样化症信息方面表现有前途,但为了患者安全和公平获取,可读性需要改进.
科学领域:
- 人工智能在医学中的应用
- 医疗保健的自然语言处理.
- 患者教育 技术 技术
背景情况:
- 氨基粉症是一种罕见,复杂的多系统性疾病,需要多学科的护理.
- 高质量的患者教育对于管理罕见疾病,如粉样性粉症至关重要.
- 像ChatGPT这样的人工智能驱动的工具为可访问的健康信息传播提供了潜力.
研究的目的:
- 评估ChatGPT的准确性,可复制性和可读性,以回答有关粉样化症的问题.
- 评估ChatGPT的实用性,作为一个工具,在amyloidosis患者和提供者教育.
主要方法:
- 98个amyloidosis问题 (心脏病学,胃肠病学,神经病学) 被输入到ChatGPT-3.5和ChatGPT-4.
- 专家医生对答案的准确性和全面性进行了分级.
- 通过重新输入问题来测试可复制性;使用美国等级来评估可读性.
主要成果:
- 聊天GPT-4实现了95%的准确性和84%的全面性;聊天GPT-3.5实现了89%的准确性和79%的全面性.
- 这两种模型在心脏病学问题上表现最好,ChatGPT-4在胃肠病学方面表现更全面.
- 聊天GPT-4的响应平均为15.5美元的读数水平,超过了患者教育建议的水平.
结论:
- 大型语言模型 (LLM) 对粉样性疾病信息有希望,但需要提高可读性.
- 未来的研究应该专注于提高LLM的准确性,并根据适当的阅读水平量身定制响应.
- 必须解决道德方面的考虑和局限性,以便在患者教育中安全和公平地实施人工智能.
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