评估人工智能驱动的患者教育:放射学中的一个案例研究
Ian J Kuckelman1, Paul H Yi2, Molinna Bui1
1University of Wisconsin School of Medicine and Public Health, 750 Highland Ave, Madison, WI 53705.
人工智能 (AI) 聊天机器人为常见的放射学检查提供准确和大部分完整的信息. 这些大型语言模型 (LLM) 显示了改善医疗成像患者教育的潜力.
科学领域:
- 医学成像和人工智能 医学成像和人工智能
- 放射学 患者教育 患者教育
背景情况:
- 患者越来越多地使用AI大型语言模型 (LLM) 来获取健康信息.
- 人工智能产生的医疗内容的准确性存在担忧.
研究的目的:
- 评估Bing聊天机器人对常见放射检查的反应的准确性和完整性.
- 评估人工智能聊天机器人在放射病患者教育中的实用性.
主要方法:
- 测试了Bing聊天机器人 (由ChatGPT驱动) 进行3次常见的放射检查 (CT腹部,MRI脊柱,骨活检).
- 在两个试验中,在三个聊天机器人设置中评估每次考试的10个问题.
- 对 radiologyinfo.org.org.org 的准确性和完整性进行了评估.
主要成果:
- 93%的答案被评为"完全正确",7%被评为"大部分正确".
- 65%的回答是"完整的",而35%的回答是"大部分完整的".
- 可读性在八年级水平;没有发现不准确或有害的信息.
结论:
- Bing 聊天机器人展示了放射病患者教育的高准确性和完整性.
- LLM聊天机器人有可能提高患者的理解,并可以集成到患者门户.
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