聊天GPT,人类放射科医生和上下文意识的聊天GPT在识别放射学报告中的AO代码方面的表现
Maximilian F Russe1, Anna Fink2, Helen Ngo2
1Department of Diagnostic and Interventional Radiology, Medical Center - University of Freiburg, Faculty of Medicine, University of Freiburg, Breisacher Str. 64, 79106, Freiburg, Germany. maximilian.russe@uniklinik-freiburg.de.
Scientific reports
|August 30, 2023
概括
人工智能聊天机器人可以快速分配 Arbeitsgemeinschaft Osteosynthesefragen (AO) 骨折代码,但需要特定的知识才能准确. 情境感知人工智能显示出改善放射学报告中骨折分类的前景.
科学领域:
- 医学成像和放射学医学成像和放射学
- 医疗保健中的人工智能
- 整形外科手术 整形外科手术
背景情况:
- 放射科医生擅长描述骨折形态,但与标准化分类扎.
- 劳动协会骨质合成骨折 (AO) 骨折和脱位分类汇编是一个复杂的系统,用于标准化骨折描述.
- 自动化骨折分类可以提高放射学报告的效率和一致性.
研究的目的:
- 评估通用和知识增强的人工智能聊天机器人在分配AO断裂代码方面的性能.
- 将聊天机器人的速度和准确性与人类读者的速度进行比较.
- 确定特定的AO分类知识对聊天机器人性能的影响.
主要方法:
- 用随机的 AO 代码生成了 100 份放射学报告.
- 测试了通用AI聊天机器人 (GPT-3.5-Turbo,GPT-4) 和知识增强的聊天机器人 (GPT-4带有矢量索引AO知识).
- 聊天机器人的性能 (速度,准确性,一致性) 与人类读者进行了比较.
主要成果:
- 聊天机器人比人类更快地分配了AO代码 (3.2s vs. 50s).
- 人类读者比最好的聊天机器人 (86%) 获得了更高的准确性 (95%).
- 与通用聊天机器人 (2%) 相比,知识增强聊天机器人表现出明显改善的一致性 (71%正确).
结论:
- 人工智能聊天机器人在AO断裂编码中提供了速度优势.
- 用特定领域的知识来增强聊天机器人,比如AO分类,可以显著提高它们的准确性和一致性.
- 情境感知人工智能代表了在放射性报告中改进人工智能工具的有希望的途径.
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