在提供心脏病治疗咨询时,伴随性疾病患者的ChatGPT性能恶化
Wen-Rui Hao1,2,3, Chun-Chao Chen1,2,3, Kuan Chen4
1Taipei Heart Institute, Taipei Medical University, Taipei 11002, Taiwan.
Healthcare (Basel, Switzerland)
|July 12, 2025
概括
大型语言模型 (LLM) 对复杂心血管疾病 (CVD) 患者的药物建议有希望,但表现不一致和安全风险. 在自主临床使用之前,专业监督至关重要.
科学领域:
- 医疗信息学 医疗信息学
- 人工智能在医学中的应用
- 心血管药理学心血管药理学
背景情况:
- 大型语言模型 (LLM) 正在被用于医学应用.
- 在复杂的患者病例中,特别是伴随疾病中,LLM在药物建议中的可靠性尚未得到充分研究.
- 本研究解决了在复杂心血管疾病 (CVD) 场景中评估LLM绩效的需要.
研究的目的:
- 系统地评估ChatGPT (GPT-3.5和GPT-4.0) 的性能,一致性和安全性,用于生成复杂心血管疾病病例的药物建议.
- 在模拟的临床场景中比较不同LLM版本的准确性和可靠性.
- 确定使用LLM用于心脏病学临床决策支持的潜在风险和局限性.
主要方法:
- 这是一项基于模拟的研究,使用25种复杂的CVD场景,每个场景都被提示10次用于GPT-3.5和GPT-4.0.0.
- 心脏病专家将建议分类为"高优先级"或"低优先级".
- 使用统计测试评估医生批准率,建议优先级,响应一致性 (贾卡德指数) 和错误模式.
主要成果:
- GPT-4.0的整体医生批准率略高 (86.90%) 比GPT-3.5 (85.06%),但对于优先级较高的推来说并不系统显著.
- 在错误模式中观察到显著差异 (p < 0.001),GPT-4.0在高风险场景中更频繁地推禁用药物.
- 较低的模型间一致性 (平均Jaccard指数=0.42) 表明LLM答案之间的建议经常有差异.
结论:
- 目前的LLM显示了高的整体医生认可,但表现不一致的性能和安全风险复杂的心血管疾病药物建议.
- 然而,LLM的可靠性尚未达到独立临床应用的标准.
- 未来的研究应该专注于现实世界的数据验证和特定领域的微调,强调专业监督的持续需求.
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