医生更信任能解释诊断依据的AI，组合框+报告效果最佳。
A User-Centric Analysis of Explainability in AI-Based Medical Image Diagnosis
- 对比多种AI解释方法，用框选区域加文字报告最易懂。
- 88%医生认为AI必须解释诊断，64%强烈认同。
- 50%医生会轻信错误的AI诊断，说明解释可信度至关重要。
近年来，医疗领域的人工智能系统已取得显著进展。尽管其性能优于人类，但因决策过程不透明，实际应用极少。最优的解释与可视化机制仍不足。为此，我们对最新的文本、视觉及多模态可解释人工智能（XAI）方法进行了以用户为中心的对比分析。对33名医生的调查显示，88%认为AI解释诊断很重要，其中64%表示非常认同。在可理解性、完整性、速度和适用性方面，框选区域结合诊断报告的方法优于其他测试的XAI方式。此外，我们还测试了错误诊断可能带来的负面影响，发现50%的参与者更信任错误的AI诊断结果，即使其解释方式已被评估为不可靠。
原文摘要 · Abstract (English)
In recent years, AI systems in the medical domain have advanced significantly. However, despite outperforming humans, they are rarely used in practice since it is often not clear how they make their decisions. Optimal explanation and visualization of the decision process are often lacking. Therefore, we conducted a comparative user-centric analysis of the latest state-of-the-art textual, visual and multimodal explainable artificial intelligence (XAI) methods for medical image diagnosis. Our survey of 33 physicians showed that 88% agree that it is important that AI explains the diagnosis -- 64% even strongly agree. A combination of bounding box and report is rated better than the other tested XAI methods in the evaluated aspects understandability, completeness, speed, and applicability. We even tested the potential negative impact of false AI-based medical image diagnoses and found that 50% of the participants trusted false AI diagnoses over all tested XAI methods.
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