聊天GPT性能用于放射学报告生成的实践评估
Mohsen Soleimani1, Navisa Seyyedi1, Seyed Mohammad Ayyoubzadeh2
1Department of Health Information Management and Medical Informatics, School of Allied Medical Sciences, Tehran University of Medical Sciences, Tehran, Iran.
Academic radiology
|August 14, 2024
概括
自然语言处理 (NLP) 模型在生成放射学报告方面取得了不同程度的成功. 在提高效率和准确度方面,ChatGPT显示出有前途,尽管对临床实践而言,仔细的模型选择至关重要.
科学领域:
- 医疗信息学 医疗信息学
- 医疗保健中的人工智能
- 自然语言处理自然语言处理.
背景情况:
- 放射学报告的生成效率低下,容易出现错误和不一致.
- 自然语言处理 (NLP) 为简化这一过程提供了潜在的解决方案.
研究的目的:
- 评估ChatGPT,一个大型语言模型 (LLM) 在生成放射学报告中的有效性.
- 为了比较各种NLP模型在模仿医生撰写的报告方面的表现.
主要方法:
- 利用了1000份来自MIMIC数据库的胸部X射线报告.
- 采用了Claude.ai用于初始关键字提取和ChatGPT用于使用三步提示符生成报告.
- 使用词汇和句子相似性技术对医生报告进行评估报告相似性.
主要成果:
- 双向和自动回归变压器 (Bart) 和跨语言语言模型 (XLM) 与医生报告具有很高的相似性 (高达99.3%).
- 解码增强的BERT与脱的注意力 (DeBERTa) 和序列匹配模型显示出较低的对齐.
- 文字嵌入模型在印象部分表现出强的表现 (84.4%的相似性).
结论:
- 在放射学报告生成的NLP模型性能中存在显著差异.
- 仔细选择和评估NLP模型对于临床应用至关重要.
- 聊天GPT展示了提高放射学报告效率和准确性的潜力.
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