让AI分析医疗评价并解释结论，提升患者信任与决策效率
Designing Explainable AI for Healthcare Reviews: Guidance on Adoption and Trust
- 用混合方法研究AI解释系统如何分析患者评价
- 82%认为能省时间，84%强调理解分类原因很重要
- 适合关注可解释性与用户信任的医疗AI设计者
患者越来越多依赖在线医疗评价选择医生，但评论数量庞大影响决策效率。本文通过混合方法研究，评估了一种可解释AI系统在分析患者评价并提供透明解释方面的表现。调查显示，60名参与者中82%认为该系统能节省时间，78%认为能突出关键信息；84%认为理解评价分类原因很重要，82%表示解释能增强信任感，约45%偏好图文结合的解释形式。开放性问题的定性分析揭示了准确、清晰、简洁、响应快、数据可信和无偏处理等核心需求。专家访谈进一步提供了技术实现上的洞察，指出不同解释方法的挑战。基于技术接受模型（TAM）与自动化信任理论，研究发现高感知有用性和透明解释促进采纳，而复杂性与不准确性则会阻碍。本文提出分层、面向受众的可解释性设计建议。
原文摘要 · Abstract (English)
Patients increasingly rely on online reviews when choosing healthcare providers, yet the sheer volume of these reviews can hinder effective decision-making. This paper summarises a mixed-methods study aimed at evaluating a proposed explainable AI system that analyses patient reviews and provides transparent explanations for its outputs. The survey (N=60) indicated broad optimism regarding usefulness (82% agreed it saves time; 78% that it highlights essentials), alongside strong demand for explainability (84% considered it important to understand why a review is classified; 82% said explanations would increase trust). Around 45% preferred combined text-and-visual explanations. Thematic analysis of open-ended survey responses revealed core requirements such as accuracy, clarity and simplicity, responsiveness, data credibility, and unbiased processing. In addition, interviews with AI experts provided deeper qualitative insights, highlighting technical considerations and potential challenges for different explanation methods. Drawing on TAM and trust in automation, the findings suggest that high perceived usefulness and transparent explanations promote adoption, whereas complexity and inaccuracy hinder it. This paper contributes actionable design guidance for layered, audience-aware explanations in healthcare review systems.
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