影响临床医生和患者与基于机器学习的风险预测模型互动的因素:系统性审查
Rebecca Giddings1, Anabel Joseph1, Thomas Callender1
1Lungs for Living Research Centre, UCL Respiratory, University College London, London, UK.
The Lancet. Digital health
|January 26, 2024
概括
医疗保健专业人员和患者通常对机器学习 (ML) 风险预测模型的看法是积极的,认识到它们的潜在好处. 然而,对数据质量和意想不到的后果的担忧仍然存在,需要进一步研究各种观点.
科学领域:
- 医疗信息学 医疗信息学
- 人工智能在医学中的应用
- 临床决策支持 临床决策支持
背景情况:
- 机器学习 (ML) 模型为医疗风险预测提供了潜在的好处.
- 目前在临床实践中对ML风险预测模型的采用仍然有限.
- 了解用户的看法对于有效实施至关重要.
研究的目的:
- 审查医疗保健专业人员 (HCP) 和患者对ML风险预测模型的看法.
- 确定影响采用和开发这些模型的因素.
- 为基于ML的医疗保健工具的未来研究和开发提供信息.
主要方法:
- 发表文章的系统文献审查.
- 进行了数据库和引用搜索.
- 分析包括了41篇文章,质量不同.
主要成果:
- 医务人员和患者对ML风险预测模型的总体看法是积极的.
- 关键问题包括数据质量,潜在的意外后果和工作量.
- 公众的看法可能比HCP更负面",一些担忧在实践中没有实现.
结论:
- 机器学习模型对医疗保健具有前景,但对其开发和使用存在保留.
- 需要进一步的研究,特别是来自代表性不足的群体,以及最佳模型解释和警报.
- 解决数据质量和意想不到的后果对于成功集成至关重要.
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