解码医学术语:在放射学报告中使用AI语言模型 (ChatGPT-4,BARD,微软副驾驶)
1Department of Radiology, King's College Hospital London, Dubai, United Arab Emirates.
Patient education and counseling
|May 14, 2024
概括
人工智能 (AI) 语言模型简化了放射学报告,以更好地理解患者. 虽然有效,但它们在紧急分类中的准确性需要进一步开发以提供全面的患者支持.
科学领域:
- 放射学和医学成像学 医学成像学
- 医疗保健中的人工智能
- 卫生沟通健康沟通
背景情况:
- 放射学报告通常包含复杂的医学术语,妨碍患者的理解.
- 有效的患者-提供者沟通对于明智的健康决策和参与至关重要.
- 人工智能语言模型为翻译复杂的医疗信息提供了潜在的解决方案.
研究的目的:
- 评估AI语言模型 (ChatGPT-4,BARD,微软Copilot) 在简化放射学报告中的有效性.
- 评估人工智能生成的内容的可读性,可理解性和可操作性.
- 为了确定人工智能驱动的急诊分类对放射学发现的准确性.
主要方法:
- 使用ChatGPT-4,BARD和微软Copilot处理了30份放射学报告.
- 使用Flesch阅读易度和Flesch-Kincaid等级水平来评估可读性.
- 使用PEMAT测量了可理解性,并评估了紧急分类准确性.
- 统计分析包括ANOVA和Chi-Square测试来比较模型性能.
主要成果:
- 所有的人工智能模型都成功地将医学术语翻译成患者友好的语言.
- 巴德显示出优异的可读性得分;ChatGPT-4和巴德在可理解性方面领先 (得分>70%).
- 人工智能模型显示紧急建议准确度的变化,没有观察到统计学上显著的差异.
结论:
- 人工智能语言模型可以显著提高患者对放射学报告的理解.
- 通过简化医疗信息,可以加强患者参与健康决策.
- 为了准确的紧急评估和全面的患者支持,人工智能模型的进一步改进是必要的.
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