开发和更新CDSS-AI整合框架,用于将医疗AI引入临床实践
Masahiko Nakatsui1,2, Yasushi Hirano1,2, Koichi Kashibe2
1Graduate School of Medicine, Yamaguchi University.
Studies in health technology and informatics
|August 8, 2025
概括
我们创建了一个新的框架,将医疗人工智能 (AI) 与临床决策支持系统 (CDSS) 和数据仓库 (DWH) 连接起来. 这提高了AI在医疗保健中的实施,提高了临床实践的可复制性和可持续性.
科学领域:
- 医疗信息学 医疗信息学
- 医疗保健中的人工智能
- 临床决策支持 临床决策支持
背景情况:
- 在临床实践中实施人工智能 (AI) 面临着可重现性,可扩展性和可持续性的挑战.
- 将人工智能工具与临床决策支持系统 (CDSS) 和数据仓库 (DWH) 等现有医疗保健基础设施集成是复杂的.
研究的目的:
- 开发一个整合性框架,连接医疗AI,CDSS和DWH.
- 提高AI实施在临床环境中的可复制性,可扩展性和可持续性.
- 为应对系统挑战,并优化AI集成效率.
主要方法:
- 开发了一个整合性框架,用于连接医疗AI,CDSS和DWH.
- 从2022年8月开始,对框架进行了运行测试.
- 利用了一种AI系统,旨在在测试期间预测罪祸首药物的不良影响.
主要成果:
- 开发的框架成功地将医疗人工智能与CDSS和DWH集成在一起.
- 运营测试表明,该框架有能力应对系统挑战.
- 该系统被优化为有效预测药物不良影响的系统.
结论:
- 整合性框架为在临床实践中实施AI提供了强大的解决方案.
- 这种方法提高了医疗AI的可持续性和可扩展性.
- 未来的工作重点是整合额外的医疗人工智能,用于更广泛的临床应用.
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