在一项负面实用试验中探索人类-人工智能相互作用,用于计算机辅助的多体检测
Kate Watkins1, Uri Ladabaum2, Esther Olsen1
1Department of Health Policy, Stanford University School of Medicine, Stanford, California, USA.
iGIE : innovation, investigation and insights
|February 6, 2026
概括
内镜中的人工智能显示了试验有效性和现实世界的有效性之间的差距. 结肠镜师对人工智能的信任并不能解释计算机辅助检测 (CADe) 试验的无效结果.
科学领域:
- 胃肠病学 胃肠病学
- 医疗人工智能 医疗人工智能
- 医疗保健服务研究 医疗服务研究
背景情况:
- 在内镜中使用人工智能 (AI) 的计算机辅助检测 (CADe) 在受控试验中显示出有前途.
- 然而,这些积极的结果并没有在实用试验中得到一致的复制,突出了疗效和有效性之间的差距.
- 了解人与人工智能的互动对于解决这种差异至关重要.
研究的目的:
- 调查结肠镜师对人工智能驱动的CADe系统的看法和经验.
- 探索在实用CADe试验中观察到的无效结果背后的原因.
- 在内镜手术的背景下,深入了解人类与人工智能的互动.
主要方法:
- 采用了一种连续,混合方法的方法,结合了调查和半结构面试.
- 结肠镜师在使用CADe之前和之后完成了调查,并在试验结果披露后再次完成了调查.
- 专题分析用于分析调查和采访的定性数据,并根据基线腺瘤检测率 (ADR) 分析了响应.
主要成果:
- 大结肠镜医生的参与度很高,参与调查的比例分别为92%和88%.
- 强大的专业身份与内镜能力 (96%) 相关,尽管试验结果为零,但对人工智能的持续信任和热情 (82%-87%) 持久.
- 大多数结肠镜医生 (62%) 对零结果感到惊,从定性数据中没有单一的解释.
结论:
- 结肠镜师对AI/CADe的信任和热情并不能解释实用试验中的零发现.
- 该研究表明,内镜中的AI可能需要超越光学识别能力,以实现其潜力.
- 对人工智能在内镜中的作用的进一步研究应该考虑超出检测准确性的因素.
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