一项实验机器学习研究,调查学生和合格的放射科医生在解释放射图像时的决策过程
Clare Rainey1, Angelina T Villikudathil1, Jonathan McConnell2
1Faculty of Life and Health Sciences, School of Health Sciences, Ulster University, York Street, Belfast, Northern Ireland, United Kingdom.
PLOS digital health
|October 25, 2023
概括
男性放射科医生,特别是那些熟练从事职业的人,对人工智能 (AI) 的信任度更高. 对人工智能的信任随着年龄和经验的增长而下降,这突出了人工智能在医疗保健中的整合关键用户特征.
科学领域:
- 医疗保健技术 医疗保健技术 医疗保健技术
- 医学成像医学成像
- 人工智能的人工智能是人工智能.
背景情况:
- 人工智能 (AI) 越来越多地被整合到医疗保健工作流程中,以减轻服务压力.
- 英国国家卫生服务局优先考虑人工智能和数字健康,但人类与这些系统的互动仍未得到充分研究.
- 在其他行业观察到的人工智能的潜在偏见,需要了解医疗保健的人工智能采用的用户特征.
研究的目的:
- 确定技能,人工智能信心和英国学生和合格放射科医生的认知知识之间的相关性.
- 探索用户特征如何影响放射科医师与临床AI系统的接触.
- 为了减轻医疗成像中人工智能实施所产生的潜在问题.
主要方法:
- 开发了一个机器学习模型,使用Boruta特征选择来预测口译员状态 (学生与合格).
- 通过Qualtrics进行的一项调查涉及解释带有和没有人工智能辅助的放射性检查.
- 参与者招募利用了通过社交媒体 (Twitter/LinkedIn) 针对英国放射科医生的方便和雪球采样.
主要成果:
- 皮尔森的相关性分析表明,熟练从事职业的男性放射科医生,与女性相比,对人工智能的信任度更高.
- 对人工智能的信任与年龄和专业经验水平都有负相关性.
- 最好的机器学习模型在识别合格的放射学家方面实现了0.93曲线下的面积和93%的预测准确性.
结论:
- 用户的特点,如性别,熟练程度,年龄和经验与放射科医生的信任和对AI的参与有显著的相关性.
- 开发的机器学习模型对识别放射科医生的专业知识非常有希望.
- 为了确定临床效用,在潜在队列中使用更大的样本大小进行进一步验证是必不可少的.
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