社区参与的人工智能研究:范围审查
Tyler J Loftus1,2, Jeremy A Balch1,2, Kenneth L Abbott2
1University of Florida Intelligent Clinical Care Center, Gainesville, Florida, United States of America.
PLOS digital health
|August 23, 2024
概括
社区参与人工智能医疗保健研究是罕见的,只有0.2%的研究涉及社区利益相关者. 参与社区可以提高AI模型的通用性和临床应用质量.
科学领域:
- 医疗保健中的人工智能
- 社区参与的研究
- 医疗信息学 医疗信息学
背景情况:
- 社区环境对于医疗保健提供至关重要,但它们在人工智能 (AI) 研究中的参与程度尚不清楚.
- 让社区参与提供了一个重要的机会,以提高AI医疗保健应用的科学质量和相关性.
- 需要进行系统的范围审查,以绘制当前知识,并确定社区参与的人工智能研究的差距.
研究的目的:
- 系统地绘制社区参与的人工智能 (AI) 医疗保健研究的景观.
- 通过社区参与,确定优化人工智能应用程序通用性的机会.
- 了解社区利益相关者和数据在人工智能模型开发,验证和实施中的作用.
主要方法:
- 对Embase,PubMed和MEDLINE数据库进行系统范围审查.
- 搜索了关于人工智能/机器学习医疗保健应用的文章,涉及社区参与模型开发,验证或实施.
- 从模型性能,社区参与性质和障碍/促进者中提取的数据,遵循PRISMA扩展范围审查指南.
主要成果:
- 在约10,880篇人工智能医疗保健文章中,只有21篇 (0.2%) 描述了社区参与.
- 所有的研究都使用了来自社区的数据,通常来自现有数据集或基于互联网的获取.
- 社区利益相关者在设计中的参与是最小的 (一项研究);小样本大小是主要障碍 (53%的研究),风险过度拟合和威胁普遍性.
结论:
- 社区参与人工智能医疗保健应用程序开发,验证和实施是非常罕见的.
- 为了提高概括性,研究人员应该让社区的利益相关者参与以用户为中心的设计,可用性和临床实施.
- 利用社区数据和利益相关者的意见对于开发在现实世界医疗保健环境中有效和相关的AI工具至关重要.
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