面向可靠医疗AI的可解释不确定性估计
Explainable Uncertainty Estimation for Reliable Medical AI
- College of Computing and Data Science (CCDS)（计算与数据科学学院）
- Nanyang Technological University (NTU)（南洋理工大学）
- A*STAR Centre for Frontier AI Research（新加坡科技研究局前沿人工智能研究中心）
- School Of Computing and Digital Technologies（计算与数字技术学院）
- Sheffield Hallam University（谢菲尔德哈勒姆大学）
- School of Computing（计算机学院）
- University of Utah（犹他大学）
- Tan Tock Seng Hospital (TTSH)（陈笃生医院）
- Lee Kong Chian School of Medicine（李光前医学院）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
该研究提出将不确定性估计与XAI统一的egRUE方法，可量化并分解不确定性为特征贡献，经实验和专家研究验证，能提升医疗AI的可靠性与可解释性，优化临床决策支持。
AI中文摘要：
人工智能具备支持临床决策的巨大潜力，但其在医疗领域的应用仍受限于信任缺失。不确定性估计可标记不可靠预测，可解释人工智能（XAI）可阐明预测的生成方式，但现有方法将二者分开处理，无法提供关于预测为何不确定的特征级洞察，也无法明确应优先进行哪些检查以降低不确定性。为解决这一缺口，我们提出可解释不确定性估计，它将不确定性估计与XAI统一，既能量化不确定性，又能解释特征级贡献。我们引入期望梯度重构不确定性估计（egRUE），该方法将预测解释纳入不确定性计算，并将不确定性分解为各特征的贡献。我们证明了egRUE的理论性质，通过实验表明，与现有方法相比，它在可靠性和可解释性方面均有提升。针对医学专家的用户研究进一步显示，egRUE的解释相比仅用不确定性评分，能提升校准后的信任度，增强对正确预测的信心，降低对错误预测的信心。通过将预测不确定性与特征级解释相结合，egRUE强化了安全关键型医疗场景中的决策支持，既明确预测可能不可靠的情况，又明确导致该不确定性的特征。
英文摘要：
Artificial intelligence has strong potential to support clinical decision-making, yet its adoption in healthcare remains limited due to a lack of trust. Uncertainty estimation can signal unreliable predictions, and explainable AI (XAI) can clarify how predictions are made but existing methods treat them separately, providing no feature-level insight into why a prediction is uncertain or which tests to prioritize to reduce it. To address this gap, we propose explainable uncertainty estimation, which unifies uncertainty estimation and XAI to both quantify uncertainty and explain feature-level contributions. We introduce the Expected Gradients Reconstruction Uncertainty Estimate (egRUE), which incorporates prediction explanations into its uncertainty computation and decomposes uncertainty into feature-wise contributions. We prove theoretical properties of egRUE and show through experiments that it improves reliability and interpretability compared to existing methods. A user study with medical experts further demonstrates that egRUE's explanations improve calibrated trust over uncertainty scores alone, increasing confidence in correct predictions and reducing confidence in incorrect ones. By combining prediction uncertainty with feature-level explanations, egRUE strengthens decision-making support in safety-critical healthcare settings, clarifying both when predictions may be unreliable and which features drive that uncertainty.
