聊天GPT提供了不一致的风险分层的患者与无创性胸痛的胸痛
Thomas F Heston1,2, Lawrence M Lewis3
1Department of Family Medicine, University of Washington School of Medicine, Seattle, Washington, United States of America.
PloS one
|April 16, 2024
概括
像ChatGPT-4这样的大型语言模型在医疗保健中显示出希望,但在胸痛患者的风险分层中表现出不一致,需要进一步细化才能获得可靠的临床使用.
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
- 在医疗保健中的自然语言处理.
背景情况:
- 像ChatGPT-4这样的大型语言模型 (LLM) 提供了潜在的医疗保健应用.
- 在分析复杂的临床数据时,LLM的可靠性和一致性尚未得到充分证实.
- 这项研究评估了ChatGPT-4在模拟患有急性非创伤性胸痛的患者风险分层方面的表现.
研究的目的:
- 为了评估与验证的临床工具相比,ChatGPT-4的风险分层精度.
- 为了评估ChatGPT-4的风险评估的一致性,当与相同的患者数据呈现时.
- 确定ChatGPT-4在分析胸痛患者复杂临床变量的可靠性.
主要方法:
- 开发了基于TIMI得分,HEART得分和44个随机临床变量的模拟案例研究的三个数据集.
- 聊天GPT-4独立对每个数据集进行了五次评分,以评估一致性.
- 将ChatGPT-4的风险分数与计算的TIMI和HEART分数进行比较,并评估了44个变量模型的协议.
主要成果:
- 聊天GPT-4与TIMI (r=0.898) 和HEART (r=0.928) 平均得分有很高的相关性.
- 观察到显著的不一致性,ChatGPT-4在45-48%的时间内为相同的分数提供了不同的风险评估.
- 对于44个变量诊断的5个ChatGPT-4模型之间的一致性仅为56%,风险得分相关性较差 (r=0.605).
结论:
- 聊天GPT-4的平均分数与既有工具相对相关,但其不一致的输出引起了临床风险评估的可靠性问题.
- 像ChatGPT-4这样的LLM需要进一步的改进和定制,以便在医疗保健中可靠地应用.
- 该研究强调了基于LLM的临床决策支持需要提高一致性,特别是在非创伤性胸痛评估方面.
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