临床决策支持系统中的可解释性与AI置信度：对乳腺癌诊疗中信任、诊断表现和认知负荷的影响
Explainability and AI Confidence in Clinical Decision Support Systems: Effects on Trust, Diagnostic Performance, and Cognitive Load in Breast Cancer Care
AI总结：
本研究探讨了临床决策支持系统中AI可解释性与置信度对乳腺癌诊疗中信任、认知负荷和诊断表现的影响，发现高置信度增加信任但导致过度依赖，低置信度引发谨慎行为，强调需平衡透明度、可用性与认知需求。
摘要：
人工智能（AI）在医疗保健领域展现出潜力，尤其是在通过临床决策支持系统（CDSSs）提升诊断准确性和决策方面。然而，这些系统的成功实施依赖于用户信任和依赖，这可能受到可解释AI的影响。本研究探讨了不同可解释性水平对临床医生信任度、认知负荷和乳腺癌检测诊断表现的影响。采用中断时间序列设计，我们开展了一项涉及28名医疗保健专业人员的网络实验。结果显示，高置信度分数大幅增加了信任，但也导致了过度依赖，降低了诊断准确性。相反，低置信度分数降低了信任度和一致性，同时增加了诊断时长，反映出更谨慎的行为。一些可解释性特征通过增加压力水平影响了认知负荷。此外，年龄、性别和职业角色等人口统计学因素塑造了参与者对系统的感知和交互。本研究为可解释性如何影响临床医生行为和决策提供了有价值的见解。研究结果强调了设计AI驱动的CDSSs以平衡透明度、可用性和认知需求的重要性，从而培养信任并促进其融入临床工作流程。
英文摘要：
Artificial Intelligence (AI) has demonstrated potential in healthcare, particularly in enhancing diagnostic accuracy and decision-making through Clinical Decision Support Systems (CDSSs). However, the successful implementation of these systems relies on user trust and reliance, which can be influenced by explainable AI. This study explores the impact of varying explainability levels on clinicians trust, cognitive load, and diagnostic performance in breast cancer detection. Utilizing an interrupted time series design, we conducted a web-based experiment involving 28 healthcare professionals. The results revealed that high confidence scores substantially increased trust but also led to overreliance, reducing diagnostic accuracy. In contrast, low confidence scores decreased trust and agreement while increasing diagnosis duration, reflecting more cautious behavior. Some explainability features influenced cognitive load by increasing stress levels. Additionally, demographic factors such as age, gender, and professional role shaped participants' perceptions and interactions with the system. This study provides valuable insights into how explainability impact clinicians' behavior and decision-making. The findings highlight the importance of designing AI-driven CDSSs that balance transparency, usability, and cognitive demands to foster trust and improve integration into clinical workflows.
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。
