多模和单模大型语言模型与多中心人类临床专家在主动脉剖析管理中的比较性能
1Department of Emergency Medicine, Etimesgut Sehit Sait Erturk State Hospital, Ankara 06790, Turkey.
Diagnostics (Basel, Switzerland)
|January 28, 2026
概括
多模式和单模式大语言模型 (LLM) 在诊断和管理大动脉剖析方面表现与人类专家相美. 需要进一步的研究来确认AI.
科学领域:
- 人工智能在医学中的应用
- 临床决策支持系统 临床决策支持系统
- 心血管紧急情况.
背景情况:
- 大型语言模型 (LLM) 提供了临床决策支持的潜力.
- 多模式LLM (MLLMs) 和单模式LLM代表了不同的AI方法.
- 人工智能模型与人类专家在大动脉剖析中的比较性能尚不清楚.
研究的目的:
- 将MLLM (GPT-4V + Med-PaLM 2 + BioGPT) 和单模LLM (ChatGPT-5.2) 的表现与人类医生的表现进行比较.
- 评估人工智能和医生在诊断,治疗和主动脉解剖并发症管理方面的表现.
- 评估AI在复杂的心血管紧急情况中的作用.
主要方法:
- 跨越五个土耳其三级保健中心的多中心横截面研究.
- 25个验证的多项选择题关于大动脉剖析 (诊断,治疗,并发症).
- 评估了人工智能模型 (MLLM,ChatGPT-5.2) 和九名医生 (放射科医生,急诊医生,心血管外科医生).
主要成果:
- 整体准确率:MLLM 92.0%,ChatGPT-5.2 96.0%,心血管外科医生 96.0%,放射科医生 92.0%,急诊医学专家 89.3%.
- 在诊断方面,MLMLM表现出色 (100%),但在治疗方面 (88.9%) 和并发症方面 (87.5%) 较低.
- 在人工智能模型和人类医生组之间没有发现统计学上显著的差异 (p > 0.05).
结论:
- 无论是MLLM还是单模式ChatGPT-5.2,都在人类专家的范围内进行了大动脉剖析场景.
- 研究结果表明,人工智能模型可以在心血管紧急情况下帮助临床决策支持.
- 需要进行更大规模的验证研究来确认人工智能与人类的等价性,并探索互补作用.
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