重要的医疗保健机器学习:从技术新性转向公平影响
Aparna Balagopalan1, Ioana Baldini2, Leo Anthony Celi3,4,5
1Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology; Cambridge, Massachusetts, United States of America.
PLOS digital health
|April 15, 2024
概括
由于结构性问题,机器学习在医疗保健领域的影响有限,优先考虑新性而不是公平的结果. 提供了建议,将重点转向机器学习为医疗保健,解决临床需求.
科学领域:
- 计算科学是一种计算科学.
- 医疗信息学医学信息学
- 医学中的人工智能
背景情况:
- 机器学习 (ML) 显示出技术上的前景,但对现实世界医疗保健的影响有限.
- 医疗保健 (MLHC) 社区的机器学习经常优先考虑技术新性,而不是有形的,公平的结果.
- 在MLHC内部存在的结构问题阻碍了实际的临床应用.
研究的目的:
- 批判性地检查MLHC领域,识别现实世界的适用性障碍.
- 要区分"对医疗保健数据进行机器学习"和"为医疗保健而进行机器学习".
- 为利益相关者提出可行的建议,以加强ML在医疗保健中的公平影响.
主要方法:
- 叙事审查利用多种国际专业知识.
- 在MLHC中分析研究环境,培训,评估指标和部署协议.
- 它的结构是"重要机器学习" (2012) 论文的医疗保健重点回声.
主要成果:
- 确定了MLHC中的结构问题,这些问题奖励技术新性而不是临床影响.
- 强调了使用医疗保健数据来解决ML挑战与使用ML来解决临床需求之间的区别.
- 检查了研究,培训,评估和部署的局限性,这些局限性阻碍了MLHC的实际应用.
结论:
- MLHC需要将重点从技术新性转移到有形,公平的临床影响.
- 解决研究,教育和部署中的系统性问题对于有效的医疗保健ML至关重要.
- 研究人员,临床医生,机构和政府之间的协作努力是必要的,以实现ML在满足医疗保健需求方面的潜力.
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