Radiographer AI study_Automation Bias and Decision Switching Datasets

AI is becoming more prevalent in healthcare across the world. As staff shortages and increased use of radiology services continue, AI has been proposed to support the heath service. The interaction of human end-users with AI is currently not well understood. This is of paramount importance when considering a future where AI will be used in patients’ care pathways.
This study investigated the impact of different forms of AI feedback on student and qualified radiographers’ diagnostic accuracy and likelihood to change their mind from their initial diagnosis. Participants were recruited from around the world and presented with different types of AI feedback (heatmaps and binary diagnosis). This study found that:
• AI feedback improves radiographers’ and student radiographers’ diagnostic accuracy on plain radiographic images, except when the AI feedback was inaccurate and in pathological cases in the student group.
• Heatmaps reduced diagnostic accuracy, while textual (binary) diagnosis had a positive impact.
• Decision switching was more prevalent in the student group
• Automation Bias was present in both student and radiographer groups but had greater prevalence in the student group.

This dataset of clinical images is restricted as they are real patient images. Access to the data may be applied for following instructions provided here

These data support the publication entitled ‘The impact of AI feedback on the accuracy of diagnosis, decision switching and trust in radiography.’

There are multiple datasets:
1) Restricted clinical images
2) Original SPSS dataset of responses to AI presentation
3) Original Excel dataset of responses (cleansed for SPSS)

This work has been funded by the College of Radiographers Research Industry Partnership Research awards scheme (CoRIPS) no. 183.

人工智能（AI）在全球医疗保健领域的应用正日益广泛。伴随着医护人员短缺问题持续存在、放射科诊疗服务使用量不断攀升，人工智能被提出用于助力医疗服务体系的运转。目前，人类终端用户与人工智能的交互机制尚未得到充分研究，而在未来人工智能将被应用于患者诊疗路径的背景下，这一问题的重要性不言而喻。

本研究探究了不同形式的人工智能反馈，对在校放射学专业学生以及在职放射技师的诊断准确性，以及其改变初始诊断结论的可能性产生的影响。研究参与者面向全球招募，实验中为其提供了两类不同的人工智能反馈形式：热图（heatmaps）与二分类诊断（binary diagnosis）结果。本研究得出以下结论：
• 人工智能反馈可提升放射技师与在校放射学专业学生对平片放射影像的诊断准确性，但当人工智能反馈存在错误，以及学生组面对病理病例时除外。
• 热图反馈会降低诊断准确性，而文本类二分类诊断反馈则可产生积极影响。
• 学生群体中更易出现决策变更行为。
• 学生与在职放射技师群体中均存在自动化偏差（Automation Bias）现象，且该现象在学生群体中的发生率更高。

本临床影像数据集因包含真实患者影像，故受到使用限制。如需获取该数据集，请遵循本页面提供的指引提交申请。
本数据集支撑题为《人工智能反馈对放射诊断准确性、决策变更与信任度的影响》的学术论文发表。
本次研究包含多份数据集：
1）受限临床影像数据集
2）人工智能反馈应答原始SPSS数据集
3）经SPSS清洗预处理的应答原始Excel数据集

本研究由英国放射技师学会研究产业合作研究奖励计划（College of Radiographers Research Industry Partnership Research awards scheme, CoRIPS）资助，项目编号183。