解码放射学报告:OpenAI ChatGPT的潜在应用,以提高患者对诊断报告的理解
Hanzhou Li1, John T Moon2, Deepak Iyer3
1Emory University School of Medicine, Department of Radiology and Imaging Science, 1364 Clifton Rd, Atlanta, GA 30322, United States of America.
聊天GPT有效地将复杂的放射学报告简化到八年级的阅读水平,为患者提高清晰度. 这种人工智能工具显著减少了报告长度,并在成像模式中提高了可读性.
科学领域:
- 医学成像分析 医学成像分析
- 在医疗保健中的自然语言处理.
- 卫生传播健康传播
背景情况:
- 诊断放射学报告往往超过美国成年人的平均阅读水平.
- 报告中复杂的医学术语可能会阻碍患者的理解.
- 改善患者对诊断成像结果的理解对于明智的医疗保健决策至关重要.
研究的目的:
- 通过各种成像方式评估诊断放射学报告的阅读复杂性.
- 评估ChatGPT在将这些报告简化到八年级阅读水平方面的有效性.
- 量化人工智能驱动的简化对报告可读性指标的影响.
主要方法:
- 随机抽取了从2022-2023年开始的400份放射学报告 (放射图,超声波,CT,MRI).
- 采用ChatGPT与一个特定的提示符来简化报告到普通人的术语.
- 计算的Flesch阅读易度得分 (FRES) 和Flesch-Kincaid阅读水平 (FKRL) 对于原始和简化报告.
主要成果:
- 原始报告的FKRL平均值为10.4±1.9,只有15%低于8.5.
- 通过ChatGPT简化的报告实现了平均FKRL为5.8±1.1,远低于8年级目标.
- 人工智能简化使报告长度平均减少61个单词,FRES增加了45.5个 (p < 0.01).
结论:
- 聊天GPT有效地简化了复杂的诊断放射学报告,使其达到患者友好的阅读水平.
- 在可读性得分 (FRES,FKRL) 和单词数量方面观察到显著的改善.
- 人工智能驱动的简化有望提高患者对医学成像结果的理解.
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