在医疗保健中应用机器学习的经济方面进行范围审查
Hanna von Gerich1, Mikael Helenius2, Iiris Hörhammer3
1Department of Nursing Science, University of Turku, Finland; Department of Health and Social Management, University of Eastern Finland, Finland.
International journal of medical informatics
|September 2, 2025
概括
这一审查突显了人工智能 (AI) 和机器学习 (ML) 在医疗保健中的经济价值的差距. 目前的研究缺乏关于人工智能的具体情况和经济细节的全面报告,这阻碍了基于证据的决策.
科学领域:
- 健康经济学
- 医疗保健中的人工智能
- 机器学习应用
背景情况:
- 人工智能和机器学习在医疗保健中的使用越来越多,需要证据证明安全性和经济价值.
- 经济评估对于医疗保健决策和资源分配至关重要.
- 本范围审查综合了当前在医疗保健中对机器学习技术的经济评估方法.
研究的目的:
- 绘制和综合现有的医疗保健中基于机器学习技术的经济评估方法.
- 发现医疗人工智能的经济评估报告和覆盖范围的差距.
- 评估当前研究与CHEERS-AI等报告准则的一致性.
主要方法:
- 根据JBI的指导进行全面的范围审查.
- 搜索了六个主要的数据库 (PubMed,CINAHL,Cochrane,Embase,Scopus,IEEE Xplore).
- 分析的18项包括使用描述性统计和诱导编码的研究,与CHEERS-AI声明进行比较.
主要成果:
- 包括18项研究,包括经济,影响和绩效评估.
- 成本效益分析是最常用的经济评估方法.
- 报告中发现了显著的差距,与CHEERS-AI平均一致率仅为39.6%,特别低于AI具体细节 (21.3%).
结论:
- 目前关于医疗保健机器技术经济评估的文献缺乏连贯性和全面覆盖.
- 需要完善人工智能开发框架,包括系统和实施后评估.
- 加强多学科合作对于经济评估的可靠性至关重要.
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