用可解释AI识别医疗虚假信息，实时替代半年专家审核。
Safeguarding Patient Trust in the Age of AI: Tackling Health Misinformation with Explainable AI
- 构建可解释AI框架，自动提取临床证据
- 95%召回率，76%准确率识别生物医学虚假信息
- 适合医疗AI安全、政策制定者和临床决策者
AI生成的健康虚假信息对全球患者安全和医疗系统信任构成前所未有的威胁。本文基于EPSRC INDICATE项目提出一种可解释AI框架，以应对医学虚假信息并提升循证医疗效率。对17项研究的系统回顾揭示了医疗领域透明AI的紧迫需求。所提方案在临床证据检索中实现95%召回率，并集成新型可信度分类器，在检测生物医学虚假信息上达到76% F1分数。结果表明，可解释AI可将传统需6个月的专家审核流程转化为实时自动化证据整合，同时保持临床严谨性。该方法为人工智能时代维护医疗体系完整性提供了关键干预手段。
原文摘要 · Abstract (English)
AI-generated health misinformation poses unprecedented threats to patient safety and healthcare system trust globally. This white paper presents an explainable AI framework developed through the EPSRC INDICATE project to combat medical misinformation while enhancing evidence-based healthcare delivery. Our systematic review of 17 studies reveals the urgent need for transparent AI systems in healthcare. The proposed solution demonstrates 95% recall in clinical evidence retrieval and integrates novel trustworthiness classifiers achieving 76% F1 score in detecting biomedical misinformation. Results show that explainable AI can transform traditional 6-month expert review processes into real-time, automated evidence synthesis while maintaining clinical rigor. This approach offers a critical intervention to preserve healthcare integrity in the AI era.
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