肺栓塞成像的放射性决策:大型语言模型的准确性和可靠性-Bing,Claude,ChatGPT和困惑
Pradosh Kumar Sarangi1, Suvrankar Datta2, M Sarthak Swarup3
1Department of Radiodiagnosis, All India Institute of Medical Sciences Deoghar, Deoghar, Jharkhand, India.
The Indian journal of radiology & imaging
|September 25, 2024
概括
人工智能 (AI) 聊天机器人在支持肺栓塞 (PE) 的成像决策方面表现出不同的准确性. 困惑在开放式问题中脱而出,而Bing在选择所有适用的问题中领先,这表明在临床集成之前需要人工智能改进.
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
- 临床决策支持系统 临床决策支持系统
背景情况:
- 人工智能聊天机器人有可能改善临床决策和简化医疗保健工作流程.
- 然而,它们在特定临床场景 (如肺栓塞) 的放射性决策支持中的作用需要进一步研究.
- 本研究评估了著名的大型语言模型 (LLM) 对PE成像建议的准确性.
研究的目的:
- 评估四个领先的LLM (Microsoft Bing,Claude,ChatGPT 3.5,Perplexity) 在提供疑似肺栓塞 (PE) 初始成像临床决策支持方面的准确性和可靠性.
- 为了比较不同问题格式的LLM的表现 (开放式与选择所有适用的).
- 评估放射科医生对LLM反应的协议的一致性.
主要方法:
- 根据美国放射学学院的适当性标准,开发了四种PE病例情景的变体.
- 三名放射科医生向LLM提出了开放式 (OE) 和选择所有适用的 (SATA) 问题.
- 通过使用类内相关系数 (ICC) 来评分,规范化和分析答案的准确性和评审者之间的可靠性.
主要成果:
- 困惑在OE问题上表现出最高准确度 (0.83),而Claude的准确度最低 (0.58).
- 对于SATA问题,Bing的准确度最高 (0.96),困惑度最低 (0.56).
- 总的来说,OE问题 (0.73) 的得分高于SATA问题 (0.68),放射科医生对SATA的认同很强 (ICC=0.875),但对OE的认同很差 (ICC=-0.067).
结论:
- 在疑似PE的临床决策支持中,LLM准确度存在显著差异.
- 困惑和Bing在不同的问题格式 (分别是OE和SATA) 中表现出强势.
- 目前的LLM不一致性需要进一步的微调和放射科医生的仔细选择,以确保可靠的临床整合.
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