诊断决策中的偏见敏感性:比较ChatGPT与居民
Henk G Schmidt1, Jerome I Rotgans2,3, Silvia Mamede4
1Erasmus School of Social and Behavioural Sciences, Erasmus University Rotterdam, Mandeville Building, Room T15-10, P.O. Box 1738, Rotterdam, DR, 3000, The Netherlands.
Journal of general internal medicine
|November 7, 2024
概括
像ChatGPT这样的人工智能聊天机器人显示出诊断支持的潜力,但容易受到患者病史中存在的偏见的影响. 虽然在准确度上相当于居民,但ChatGPT.
科学领域:
- 医疗信息学 医疗信息学
- 医疗保健中的人工智能
- 临床决策支持 临床决策支持
背景情况:
- 诊断错误显著影响患者的护理,往往源于临床推理中的认知偏见.
- 人工智能 (AI) 聊天机器人,如ChatGPT,是减轻这些偏见的潜在工具.
- 然而,人工智能聊天机器人对诊断偏差的敏感性仍然在很大程度上是未知的.
研究的目的:
- 为了评估与住院医生相比,ChatGPT的诊断准确度.
- 评估ChatGPT对临床推理中各种类型的认知偏差的易感性.
- 为了确定AI聊天机器人是否可以作为可靠的诊断支持工具.
主要方法:
- 这项研究将ChatGPT的诊断性能与5个偏见诱导实验中的265名医疗人员进行了比较.
- 调查的偏见包括案例内在因素 (分散注意力的发现,破坏性患者行为) 和情境因素 (类似案件的先前可用性).
- 诊断准确度是通过ChatGPT识别最有可能诊断的能力来衡量的.
主要成果:
- 聊天GPT的整体诊断准确度相当于医疗人员的诊断准确度.
- 在面对案例内在偏见时,ChatGPT和居民都显示准确度下降.
- 聊天GPT (4.0和3.5) 表现出对患者病史中的偏见的敏感性,而其表现不受局势偏见的影响,与居民不同.
结论:
- 聊天GPT对患者病史中嵌入的偏见敏感,但对情境偏见不敏感.
- 虽然人工智能聊天机器人具有诊断支持的潜力,但由于偏见易感性,它们的使用需要谨慎.
- 需要进一步的研究来增强AI的偏差检测和缓解能力,以获得可靠的临床应用.
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