大型语言模型的资源利用率与诊断准确度之间的关系,用于放射性图像解释中的高效多式联络推理
Taewon Han1, Jeong Hyun Lee1, Woo Kyoung Jeong1
1Department of Radiology and Center for Imaging Sciences, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, the Republic of Korea.
European journal of radiology
|January 23, 2026
概括
多模式推理模型显示,用描述性文本提高了诊断准确性,但快速优化是提高效率的关键. 模型架构决定了基线性能,同时提示在这些限制内改进效率.
科学领域:
- 医疗成像中的人工智能
- 放射性图像解释技术 放射性图像解释技术
- 多模式人工智能模型
背景情况:
- 放射性图像的解释依赖于复杂的数据分析.
- 多模式推理模型 (MRM) 提供了增强诊断能力的潜力.
- 评估MRM性能和资源效率对于临床整合至关重要.
研究的目的:
- 评估三种MRM的诊断性能.
- 评估这些模型的资源效率 (代币消耗).
- 在不同的输入条件下 (仅图像与组合描述文本) 和提示类型 (基本,原始,思维链[CoT]) 下比较性能.
主要方法:
- 在73例放射病例中测试了三种MRM.
- 用多选题 (MCQ) 和差异诊断 (DDx) 任务来评估表现.
- 测量了代币消费以评估资源利用率,并使用皮尔森相关性来分析准确度-代币关系.
主要成果:
- 在仅用于成像的MCQ中,o1模型实现了56.2%的准确性,超过了人类基准 (55.9%).
- 添加描述性文本显著提高了所有模型的性能,o1达到71.2%的准确性.
- 原始提示减少了输出令牌而不会影响o1的准确性,同时在总令牌和准确性之间发现了强大的正相关性.
结论:
- 模型架构从根本上影响了基线诊断性能.
- 快速工程优化了在MRM架构固有的约束范围内的效率.
- 在放射学中,MRM表现有前途,通过多式联络输入和快速策略提高性能.
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