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人工智能反馈对放射诊断准确性、决策转换及信任度的影响。

The impact of AI feedback on the accuracy of diagnosis, decision switching and trust in radiography.

作者信息

Rainey Clare, Bond Raymond, McConnell Jonathan, Gill Avneet, Hughes Ciara, Kumar Devinder, McFadden Sonyia

机构信息

Ulster University, School of Health Sciences, York St, Northern Ireland.

Ulster University, School of Computing, York St, Northern Ireland.

出版信息

PLoS One. 2025 May 9;20(5):e0322051. doi: 10.1371/journal.pone.0322051. eCollection 2025.

DOI:10.1371/journal.pone.0322051
PMID:40344152
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC12064023/
Abstract

Artificial intelligence decision support systems have been proposed to assist a struggling National Health Service (NHS) workforce in the United Kingdom. Its implementation in UK healthcare systems has been identified as a priority for deployment. Few studies have investigated the impact of the feedback from such systems on the end user. This study investigated the impact of two forms of AI feedback (saliency/heatmaps and AI diagnosis with percentage confidence) on student and qualified diagnostic radiographers' accuracy when determining binary diagnosis on skeletal radiographs. The AI feedback proved beneficial to accuracy in all cases except when the AI was incorrect and for pathological cases in the student group. The self-reported trust of all participants decreased from the beginning to the end of the study. The findings of this study should guide developers in the provision of the most advantageous forms of AI feedback and direct educators in tailoring education to highlight weaknesses in human interaction with AI-based clinical decision support systems.

摘要

人工智能决策支持系统已被提议用于协助英国陷入困境的国民医疗服务体系（NHS）工作人员。其在英国医疗系统中的实施已被确定为优先部署事项。很少有研究调查此类系统的反馈对最终用户的影响。本研究调查了两种形式的人工智能反馈（显著性/热图和带有置信度百分比的人工智能诊断）对学生和合格的诊断放射技师在确定骨骼X光片二元诊断时的准确性的影响。结果表明，除了人工智能诊断错误的情况以及学生组中的病理病例外，人工智能反馈在所有情况下都有助于提高准确性。从研究开始到结束，所有参与者自我报告的信任度都有所下降。本研究的结果应指导开发者提供最有利的人工智能反馈形式，并指导教育工作者调整教育方式，以突出人类与基于人工智能的临床决策支持系统交互中的弱点。

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