人工智能与急诊医生:谁能更好地诊断?
Ali İhsan Kilci1, Ramazan Azim Okyay2, Erhan Kaya2
1Kahramanmaraş Sütçü İmam University, Emergency Medicine - Kahramanmaraş, Turkey.
概括
大型语言模型 (LLM) 在模拟的紧急环境中显示出比人类专家更高的诊断准确性. 在选择初始诊断测试时,LLM的表现与专家相似,这表明AI是潜在的决策支持工具.
科学领域:
- 紧急医疗 紧急医疗
- 人工智能的人工智能
- 医学诊断 医学诊断 医学诊断
背景情况:
- 紧急服务部门面临的诊断挑战.
- 大型语言模型 (LLM) 是新兴的AI工具,具有潜在的临床应用.
- 与人类专家对比LLM绩效的评估对于采用至关重要.
研究的目的:
- 为了比较LLMs的诊断准确度与紧急医疗专家.
- 评估LLM在选择初始诊断测试方面的能力.
- 在模拟的急诊室场景中评估LLM.
主要方法:
- 创建模拟的紧急情况介绍 (病史,考试结果).
- 对比了人类专家和三个LLM (ChatGPT-4,4o,3.5-mini) 的诊断准确性和测试选择.
- 根据预定义的正确诊断和适当的第一线测试来评估准确性.
主要成果:
- 与人类专家 (92%) 相比,LLMs的诊断准确度更高 (97-99%).
- 在初始诊断测试选择中,LLM的表现与人类专家相似 (80-89%与88%).
- 大多数诊断错误涉及心血管和胃肠道病例.
结论:
- LLM 显示可接受的诊断准确性,超过了人类专家的性能.
- 在初始测试选择中,LLM的表现与人类专家的表现相当.
- 人工智能模型在紧急医疗中显示出作为决策支持工具的前景,保证进一步的现实世界的验证.
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