警告人工智能关于认知偏差。

IF 3.1 3区 医学 Q2 HEALTH CARE SCIENCES & SERVICES
Medical Decision Making Pub Date : 2025-10-01 Epub Date: 2025-06-24 DOI:10.1177/0272989X251346788
Jonathan Wang, Donald A Redelmeier
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引用次数: 0

摘要

人工智能模型在提供医疗建议时显示出类似人类的认知偏差。我们测试了一个明确的预警,“请记住认知偏差和其他推理陷阱”,是否可以减轻OpenAI的生成式预训练转换大型语言模型中的偏差。我们使用了10个临床细微差别的病例来测试有或没有预警的特定偏差。预警组的回答比对照组长50%,讨论认知偏差的频率是对照组的100多倍。尽管存在这些差异,但预警只减少了6.9%的总体偏倚,并且没有完全消除偏倚。这些发现强调了临床医生在解释看似深思熟虑和深思熟虑的反应时需要保持警惕。可以警告人工智能模型避免种族和性别偏见。预先警告人工智能模型以避免认知偏差并不能充分减轻推理的多重陷阱。批判性推理仍然是执业医师的一项重要临床技能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Forewarning Artificial Intelligence about Cognitive Biases.

Forewarning Artificial Intelligence about Cognitive Biases.

Forewarning Artificial Intelligence about Cognitive Biases.

Forewarning Artificial Intelligence about Cognitive Biases.

Artificial intelligence models display human-like cognitive biases when generating medical recommendations. We tested whether an explicit forewarning, "Please keep in mind cognitive biases and other pitfalls of reasoning," might mitigate biases in OpenAI's generative pretrained transformer large language model. We used 10 clinically nuanced cases to test specific biases with and without a forewarning. Responses from the forewarning group were 50% longer and discussed cognitive biases more than 100 times more frequently compared with responses from the control group. Despite these differences, the forewarning decreased overall bias by only 6.9%, and no bias was extinguished completely. These findings highlight the need for clinician vigilance when interpreting generated responses that might appear seemingly thoughtful and deliberate.HighlightsArtificial intelligence models can be warned to avoid racial and gender bias.Forewarning artificial intelligence models to avoid cognitive biases does not adequately mitigate multiple pitfalls of reasoning.Critical reasoning remains an important clinical skill for practicing physicians.

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来源期刊
Medical Decision Making
Medical Decision Making 医学-卫生保健
CiteScore
6.50
自引率
5.60%
发文量
146
审稿时长
6-12 weeks
期刊介绍: Medical Decision Making offers rigorous and systematic approaches to decision making that are designed to improve the health and clinical care of individuals and to assist with health care policy development. Using the fundamentals of decision analysis and theory, economic evaluation, and evidence based quality assessment, Medical Decision Making presents both theoretical and practical statistical and modeling techniques and methods from a variety of disciplines.
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