构建膝盖超声报告的大型语言模型-对DeepSeek R1,Gemini 2.5闪光灯和GPT-4o进行比较评估
Min Tang1, Le Xu1, Libiao Zhu2
1Department of Ultrasound, The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, Anhui, 230001, China.
BMC medical informatics and decision making
|March 2, 2026
概括
与DeepSeek R1和Gemini 2.5 Flash相比,GPT-4o在从膝盖超声报告中生成结构化,临床解释的输出方面表现出卓越的表现. 这表明它有可能提高超声波报告的一致性和临床实用性.
科学领域:
- 医学成像分析分析 医学成像分析
- 医疗保健中的人工智能
- 自然语言处理 (NLP) 是一种自然语言处理.
背景情况:
- 自由文本临床报告在数据提取和标准化方面带来了挑战.
- 大型语言模型 (LLM) 提供了自动化解读非结构化医疗数据的潜力.
- 在像肌肉骨超声波这样的专业医疗领域评估LLM绩效至关重要.
研究的目的:
- 为了比较三个领先的LLM (DeepSeek R1,Gemini 2.5 Flash,GPT-4o) 在处理膝盖超声报告中的有效性.
- 评估LLM能够从自由文本报告中生成结构化,临床相关的输出的能力.
- 评估LLM产生的诊断印象和临床建议.
主要方法:
- 对359份膝盖超声报告进行了回顾性分析.
- 使用了四种快速策略来提取信息,结构化报告,印象生成和推生成.
- 采用基于医生的主观评分和客观指标来评估绩效,包括方案合规性,可重复性和诊断准确性.
主要成果:
- 在所有评估任务中,GPT-4o显示了最高的整体性能,包括命名实体识别 (NER) 和诊断总结.
- 与DeepSeek R1 (0.54) 和Gemini 2.5 Flash (0.45) 相比,GPT-4o实现了更高的诊断性能 (F1得分=0.79).
- DeepSeek R1显示出具有竞争力的结构化报告能力,而Gemini 2.5 Flash在NER准确性等关键领域表现不佳.
结论:
- 在从膝盖超声报告中生成临床解释的输出方面,GPT-4o始终优于DeepSeek R1和Gemini 2.5 Flash.
- 这些发现支持GPT-4o提高肌肉骨超声报告的一致性和临床实用性的潜力.
- 进一步的研究可以探索将先进的LLM集成到临床工作流程中,以加强医疗报告分析.
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