人-人工智能集体最准确地诊断出临床细节
Nikolas Zöller1, Julian Berger1, Irving Lin2
1Center for Adaptive Rationality, Max Planck Institute for Human Development, Berlin 14195, Germany.
概括
混合人工智能系统结合了人类专业知识和大型语言模型 (LLM) 改善了医疗诊断. 这种集体智能方法通过利用互补的优势来提高准确性,超过人类和人工智能.
科学领域:
- 人工智能的人工智能
- 医学诊断 医学诊断 医学诊断
- 集体情报是一种集体情报.
背景情况:
- 大型语言模型 (LLM) 越来越多地用于高风险的决策.
- LLM有诸如幻觉,缺乏常识和偏见等局限性.
- 由于这些局限性,对复杂决策仅依赖LLM是有问题的.
研究的目的:
- 开发和评估一个混合集体情报系统,整合人类医生和法学士.
- 为了减轻与LLM在高风险决策中的使用相关的风险.
- 为了提高开放式医学诊断的准确性.
主要方法:
- 创建了一个混合系统,将医生差异诊断与LLM诊断相结合.
- 该系统分析了40,762个医生诊断和5个最先进的LLM诊断.
- 这项研究使用了2,133个基于文本的医疗病例小贴士进行评估.
主要成果:
- 医生和法学士的混合集体显著超过了个人医生和法学士.
- 在各种医疗专业和经验水平上观察到绩效的改善.
- 增强的准确性归因于人类和LLMs的互补错误模式.
结论:
- 混合集体情报系统可以有效地利用人类和LLMs的互补优势.
- 这种方法为改善医疗诊断等复杂,开放领域的准确性提供了有希望的解决方案.
- 将人类经验与人工智能能力相结合,可以提高决策的安全性,质量和公平性.
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