不确定性之影：AI不确定性可视化如何影响阿尔茨海默病预测的可信度
Shades of Uncertainty: How AI Uncertainty Visualizations Affect Trust in Alzheimer's Predictions
AI总结：
本研究探讨了AI不确定性可视化对阿尔茨海默病预测可信度的影响，发现连续编码提升可靠性，而二元编码增强即时信心，揭示了高不确定性下专业背景的权衡。
摘要：
人工智能（AI）越来越多地用于支持阿尔茨海默病（AD）的预后，但其采用仍然受限于缺乏透明性和可解释性，特别是在长期预测中，不确定性是固有的，而结果可能需要数年才能确定。我们将不确定性可视化定位为一种可解释人工智能（XAI）技术，并研究它如何在用户解读AI生成的未来认知衰退过渡预测时影响信任、信心和依赖。我们进行了两项研究，一项针对一般参与者（N=37），另一项针对神经影像学和神经学专家（N=10），以比较二元（存在/不存在）和连续（饱和）不确定性编码。连续编码提高了感知的可靠性，帮助用户识别模型的局限性，而二元编码增加了即时信心，揭示了在高不确定性下解读未来预测时专业知识相关的权衡。这些发现揭示了在为预后AI设计不确定性表示方面的关键挑战，并最终得出了一套基于实证的指南，用于创建可信、适合用户的临床决策支持工具。
英文摘要：
Artificial intelligence (AI) is increasingly used to support prognosis in Alzheimer's disease (AD), but adoption remains limited due to a lack of transparency and interpretability, particularly for long-term predictions where uncertainty is intrinsic and outcomes may not be known for years. We position uncertainty visualization as an explainable AI (XAI) technique and examine how it shapes trust, confidence, and reliance when users interpret AI-generated forecasts of future cognitive decline transitions. We conducted two studies, one with general participants (N=37) and one with experts in neuroimaging and neurology (N=10), to compare binary (present/absent) and continuous (saturation) uncertainty encodings. Continuous encodings improved perceived reliability and helped users recognize model limitations, while binary encodings increased momentary confidence, revealing expertise-dependent trade-offs in interpreting future predictions under high uncertainty. These findings surface key challenges in designing uncertainty representations for prognostic AI and culminate in a set of empirically grounded guidelines for creating trustworthy, user-appropriate clinical decision support tools.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
