报告放射学家与人工智能的互动-不同形式的AI反如何影响信任和决策切换?
Clare Rainey1, Raymond Bond2, Jonathan McConnell3
1Ulster University, School of Health Sciences, York St, Belfast, Northern Ireland.
PLOS digital health
|August 7, 2024
概括
放射学中的人工智能 (AI) 表明,同意AI反,特别是热图,增加了临床医生的信任. 可解释的人工智能需要精确的本地化,以确保道德的患者护理和有效的实施.
科学领域:
- 放射学 放射学是一门学科.
- 人工智能的人工智能
- 医疗成像医学成像
背景情况:
- 人工智能在医疗保健中的整合,特别是放射学,有助于图像解释和报告.
- 临床医生的信任是人工智能有效临床实施的关键因素.
- 可解释的AI (XAI) 方法,如热图,旨在提高AI的透明度和信任.
研究的目的:
- 量化不同AI反形式对专家临床医生的信任的影响.
- 调查人工智能生成的热图和二进制诊断对信任的影响.
- 评估人工智能接口设计在国际上的潜在影响.
主要方法:
- 在肌肉骨放射 (MURA) 数据集上训练了一个卷积神经网络.
- 报告的放射学家 (n=12) 评估了AI反,包括热图 (GradCAM) 和二进制诊断.
- 记录了对AI反的信任感知和决策切换.
主要成果:
- 临床医生在86.7%的检查中同意AI二元反.
- 与AI热图局部化病理学的一致性为22.2%.
- 与人工智能反 (热图和二进制诊断) 达成更高的共识与信任度的增加正相关.
结论:
- 与人工智能反,特别是热图的同意,与更大的临床医生信任有关.
- 可解释的人工智能必须优先考虑准确的本地化,以培养适当的信任.
- 这项研究对全球放射学领域的AI接口设计有影响.
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