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对基因综合征的深度学习和基于XAI的面部表型工具的评估:临床用户研究
Ömer Sümer1, Tobias Huber2, Dat Duong3
1Human-Centered Artificial Intelligence, University of Augsburg, Augsburg, Germany.
medRxiv : the preprint server for health sciences
|June 26, 2025
概括
可解释的人工智能 (XAI) 提高了诊断准确度和临床医生对遗传测试的信心,即使用户最初持怀疑态度. 这种人工智能 (AI) 工具有助于通过面部图像分析来诊断罕见的遗传疾病.
科学领域:
- 临床遗传学 临床遗传学
- 人工智能的人工智能
- 医学诊断 医学诊断 医学诊断
背景情况:
- 人工智能 (AI) 工具在临床遗传学中越来越多地用于通过患者照片来诊断遗传疾病.
- 可解释性AI (XAI) 提供可解释的输出,如突出性地图,对于监管合规和临床医生对AI的理解至关重要.
- 在现实世界中,XAI对临床医生的表现,对人工智能诊断工具的信心和信任的影响在很大程度上仍未得到审查.
研究的目的:
- 评估人工智能支持的诊断对医学遗传学家的影响,而不是XAI支持的诊断.
- 评估XAI如何影响临床医生的表现,对人工智能驱动的基因诊断的信心和信任.
- 探索XAI,诊断准确度和临床医生的决策在罕见遗传疾病的面部表型化之间的关系.
主要方法:
- 进行了一项基于网络的用户实验,涉及31名医学遗传学家.
- 参与者被随机分配到只有AI或XAI支持的诊断组.
- 在AI/XAI暴露前后使用18张面部图像 (遗传综合征和未受影响的个体) 进行诊断任务.
主要成果:
- 只有AI和XAI的方法都提高了诊断准确性和临床医生的信心.
- 人工智能和XAI的影响因人工智能预测准确度和样本难度 (综合征特征清晰度) 而有所不同.
- 虽然用户最初对XAI持怀疑态度,但在诊断改善和XAI干预之间观察到正相关性;XAI促使批判性图像评估并影响了信心.
结论:
- 人工智能和XAI支持积极影响诊断准确性和临床医生对遗传诊断的信心.
- 尽管最初持怀疑态度,但XAI鼓励对AI预测进行批判性评估,并影响临床医生的信任和信心.
- 研究结果强调了临床人工智能工具中信任,感知效用和可解释性的复杂相互作用,指导了未来罕见疾病诊断的发展.
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