使用ChatGPT将自由文本放射学报告转化为结构化报告:关于甲状腺超声波的研究
Huan Jiang1, ShuJun Xia1, YiXuan Yang1
1Department of Ultrasound, Ruijin Hospital, Shanghai Jiaotong University School of Medicine, 197 Ruijin Er Road, 200025 Shanghai, China; College of Health Science and Technology, Shanghai Jiao Tong University School of Medicine, 227 Chongqing South Road, 200025, Shanghai, China.
European journal of radiology
|April 13, 2024
概括
这项研究评估了ChatGPT结构化甲状腺超声报告的能力. 与ChatGPT-3.5.5相比,ChatGPT-4.0在结节分类和管理建议方面表现出更高的准确性.
科学领域:
- 放射学 放射学是一门学科.
- 人工智能的人工智能
- 医疗信息学 医疗信息学
背景情况:
- 结构化的放射学报告对于有效的数据提取和专业间的协作至关重要.
- 像ChatGPT这样的大型语言模型 (LLM) 在医疗报告中的应用是一个新兴的研究领域.
研究的目的:
- 根据ACR-TIRADS指南,评估ChatGPT (3.5和4.0版本) 在生成结构化甲状腺超声波报告中的准确性和可重复性.
- 为了比较ChatGPT-3.5和ChatGPT-4.0在构建这些报告中的性能.
主要方法:
- 对136份甲状腺超声波报告 (184个结节) 进行了回顾性分析.
- 使用ChatGPT-3.5和ChatGPT-4.0来根据ACR-TIRADS标准对报告进行结构化.
- 两名放射科医生评估了结节分类的质量,准确性和管理建议,每个报告都经过两次处理以评估一致性.
主要成果:
- 与ChatGPT-3.5 (34.5%) 相比,ChatGPT-4.0在甲状腺结节分类方面显示出明显更高的准确性 (69.3%).
- 与ChatGPT-3.5.5相比,ChatGPT-4.0提供了更准确和更全面的管理建议.
- 在结节分类方面,ChatGPT-4.0表现出比ChatGPT-3.5 (ICC = 0.429) 更大的一致性 (ICC = 0.732).
结论:
- 聊天GPT具有将非结构化甲状腺超声波报告转换为结构化格式的巨大潜力.
- 与ChatGPT-3.5.5.0相比,ChatGPT-4.0在甲状腺结节分类和管理建议的准确性和一致性方面提供了卓越的性能.
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