人工智能思维链推理在细微的医疗场景中:通过模型不妥协来缓解认知偏见
Jonathan Wang1, Donald A Redelmeier2
1Department of Medicine, University of Toronto, Toronto, Ontario, Canada.
BMJ quality & safety
|November 24, 2025
概括
连锁思维推理大语言模型 (LLM) 显示持久的认知偏见,并在医疗场景中产生不灵活的建议. 临床医生在使用人工智能为患者护理时必须保持警.
科学领域:
- 人工智能的人工智能
- 医疗信息学 医疗信息学
- 认知科学 认知科学
背景情况:
- 大型语言模型 (LLM) 越来越多地被纳入临床决策.
- LLM可能会表现出类似人类的认知偏见,影响细微的医疗选择.
研究的目的:
- 评估LLM中的思维链推理是否可以减轻医生观察到的认知偏见.
- 评估与人类医生相比,LLM反应中的偏见,不妥协和偏差的程度.
主要方法:
- 十个医疗场景以偏见的版本向LLM (DeepSeek,OpenAI,Google) 提供.
- 偏见是通过响应差异来衡量的;不妥协是通过香农;偏差是通过与2507名医生的响应进行比较来衡量.
主要成果:
- 一个LLM减轻了10个偏见中的6个,但产生了不妥协的反应.
- 特定的偏见 (后期谬误,诱效应,奥卡姆的剃须刀,事后见解) 在模型中持续存在.
- 在所有场景中,LLM的反应与执业医生的反应有显著的偏离.
结论:
- 连锁思维推理的LLM仍然表现出持久的认知偏见和不妥协的建议.
- 临床医生必须批判性地评估AI输出,考虑复杂的医疗决策中的潜在偏见.
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