Artificial intelligence (AI)-driven decision support systems can improve diagnostic accuracy and efficiency in computational pathology. However, collaboration between human experts and AI may introduce cognitive biases such as automation and anchoring bias, where users adopt system predictions blindly or are disproportionately influenced by AI advice, even when inaccurate. These effects may be amplified under time pressure, common in routine pathology, or shaped by individual user characteristics. We conducted an online experiment in which pathology experts (n = 28) estimated tumor cell percentages: once independently and once with AI support. A subset of estimations in each condition was performed under time strain. Overall, AI assistance improved diagnostic performance but introduced a 7% automation bias rate, defined as accepted negative consultations where previously correct independent judgments were overturned by incorrect AI advice. While time pressure did not increase the frequency of automation bias, it appeared to intensify its severity, reflected in stronger performance declines associated with increased AI reliance under cognitive load. A linear mixed-effects model (LMM) simulating weighted averaging showed a statistically significant positive coefficient for AI advice, indicating moderate anchoring on system output. This effect increased under time pressure, suggesting anchoring bias becomes more pronounced when cognitive resources are limited. A second LMM assessing automation reliance, a proxy for automation and anchoring bias, showed that professional experience and self-efficacy were associated with lower dependence on AI, whereas higher confidence during AI-assisted decisions was tied to increased AI reliance. These findings highlight the dual nature of AI integration in clinical workflows: improving performance while introducing risks of bias-driven diagnostic errors.

翻译：人工智能驱动的决策支持系统能够提升计算病理学诊断的准确性与效率。然而，人类专家与人工智能的协作可能引发自动化偏差与锚定效应等认知偏差——用户可能盲目采纳系统预测，或受到AI建议的过度影响，即使该建议存在错误。这些效应在常规病理工作中常见的时间压力下可能被放大，或受个体用户特征影响。我们开展了一项在线实验，邀请病理学专家（n = 28）分两次评估肿瘤细胞百分比：首次独立评估，第二次在AI辅助下评估。每种条件下均有部分评估在时间压力下完成。总体而言，AI辅助提升了诊断性能，但引发了7%的自动化偏差率（定义为接受负面咨询的情况：先前正确的独立判断被错误的AI建议推翻）。时间压力虽未增加自动化偏差的发生频率，但加剧了其严重程度，表现为认知负荷下随着AI依赖度增加而出现的更显著性能下降。通过线性混合效应模型模拟加权平均过程发现，AI建议系数呈统计学显著正相关，表明存在对系统输出的中度锚定效应。该效应在时间压力下增强，提示认知资源受限时锚定偏差更为明显。第二个评估自动化依赖度的LMM（作为自动化与锚定偏差的代理指标）显示：专业经验与自我效能感与较低的AI依赖度相关，而AI辅助决策期间更高的信心水平则与AI依赖度上升相关。这些发现揭示了AI融入临床工作流程的双重性：在提升诊断性能的同时，也可能引发由认知偏差导致的诊断错误风险。