在应用到区域麻醉技术 (sonoanatomy) 时,ChatGPT-4的可靠性
Engin I Turan1, Abdurrahman E Baydemir2, Zehra P Turan3
1Department of Anesthesiology, Istanbul Health Science University Kanuni Sultan Süleyman Education and Training Hospital, Istanbul, Türkiye - enginihsan@hotmail.com.
Minerva anestesiologica
|February 9, 2026
概括
像ChatGPT-4这样的大型语言模型通过准确识别解剖学标志和预测阻断成功来改善超声波导向区域麻醉的前景,帮助麻醉师在外科护理中获得成功.
科学领域:
- 医疗成像医学成像
- 人工智能在医学中的应用
- 区域麻醉地区麻醉
背景情况:
- 超声导向区域麻醉对于外科手术期间的止痛至关重要,但由于操作员在解剖学里程碑识别中的变异性而面临挑战.
- 人工智能 (AI),特别是大型语言模型 (LLM),为这些程序的实时翻译支持提供了潜在的解决方案.
研究的目的:
- 评估ChatGPT-4在解释区域麻醉的超声波图像中的准确性.
- 评估LLM在识别解剖结构,分类区块类型和预测区块成功方面的能力.
主要方法:
- 一项前性观察性研究包括111名接受超声波导向区域麻醉的成年患者.
- 使用ChatGPT-4分析了手术内超声波图像,并使用特定提示来识别里程碑,区块分类和成功评估.
- 模型输出与专家麻醉师的评估进行了比较.
主要成果:
- 在147张分析图像中,ChatGPT-4在识别解剖学地标 (95.9%) 和预测块成功 (93.9%) 方面取得了很高的准确性.
- 区块类型分类的准确性为72.8%,对于TAP,ESP和股骨神经块等常见的区块的性能更高.
- 精度有所不同,对于不太常见或更复杂的块类型,精度较低.
结论:
- 在超声波导向区域麻醉中,ChatGPT-4显示出作为决策支持工具的巨大潜力.
- 该模型在里程碑识别和成功预测方面的高准确性支持其融入临床工作流程.
- 进一步开发可能会提高分类不同类型块的性能.
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