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人工智能在热门位置:大语言模型和心脏病学家在紧急情况下的对比比较
Vedat Cicek1, Lili Zhao2, Yalcin Tur1,3
1Machine & Hybrid Intelligence Lab, Department of Radiology, Northwestern University, Chicago, IL 60611, USA.
Medical sciences (Basel, Switzerland)
|January 21, 2026
概括
大型语言模型 (LLM) 在心脏紧急情况中表现不同. 聊天GPT在模拟的导管实验室场景中匹配了职业生涯早期的干预心脏病学家,表明了作为决策支持工具的潜力.
科学领域:
- 人工智能在医学中的应用
- 心血管干预 心血管干预
- 医疗决策支持 医疗决策支持
背景情况:
- 大型语言模型 (LLM) 在关键心脏紧急情况中的临床实用性尚不清楚.
- 本研究评估了复杂的导管实验室 (Cath实验室) 场景中的LLM性能.
研究的目的:
- 评估和比较高级LLM与干预心脏病专家在管理模拟心脏紧急情况方面的表现.
- 为了确定哪些LLM在高风险的Cath实验室场景中表现最好.
主要方法:
- 一项涉及12种下心肌梗塞情景的横截面研究.
- 七名LLM和五名职业生涯早期的干预心脏病学家对模拟病例做出了反应.
- 答案是匿名的,并由30名经验丰富的干预心脏病学家进行评估.
主要成果:
- 医生获得的平均得分为80.68.
- 聊天GPT获得了最高的LLM评分 (87.4),超过了早期职业生涯的医生 (p < 0.05).
- 克劳德 (80.8) 和DeepSeek (78.7) 与医生没有显著差异,而双子座,LLAMA,Qwen和Bing的表现明显差.
结论:
- 对心脏紧急情况的LLM性能存在显著的变化.
- 在模拟的Cath实验室环境中,ChatGPT的表现与职业生涯早期的干预心脏病学家相提并论.
- 在干预性心脏病学中,LLM显示出作为补充决策支持工具的前景.
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