将人工智能平台集成到临床IT中:用于临床AI模型开发的BPMN流程
Kfeel Arshad1, Saman Ardalan1, Björn Schreiweis2,3
1Institute for Medical Informatics and Statistics, Kiel University and University Hospital Schleswig-Holstein, Kiel, Germany.
BMC medical informatics and decision making
|July 3, 2025
概括
一个新的临床人工智能 (AI) 平台将AI模型开发集成到临床IT基础设施中. 这个平台简化了AI生命周期,从数据选择到推断,用于各种医疗保健应用.
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IT整合 临床IT整合
背景情况:
- 人工智能 (AI) 在医院中的使用越来越多,导致孤立的解决方案没有集成到临床IT中.
- 需要一个临床人工智能平台来管理AI模型开发生命周期在临床IT基础设施内.
研究的目的:
- 研究将临床人工智能平台集成到现有的临床IT基础设施中.
- 在临床IT中概述AI模型开发周期的阶段.
- 用业务流程模型和标记 (BPMN) 图来说明系统景观交互.
主要方法:
- 对临床人工智能平台进行了需求分析,考虑了临床IT的具体情况.
- 使用BPMN图表识别和映射整个AI模型开发周期的流程.
- 评估了使用FEDS框架的三个临床用例的识别过程.
主要成果:
- 开发了BPMN流程图,涵盖了整个临床AI模型开发周期:数据选择,数据注释,现场培训和测试以及推断 (批量和实时).
- 通过三个临床用例证明,该方法适用于广泛的医疗保健AI应用.
- 成功评估流程,证实了开发方法的全面性质.
结论:
- 临床AI平台适用于临床IT中的本地AI模型开发.
- BPMN图表有效地涵盖了各种临床AI使用案例.
- 该框架支持未来的进步,如多站点人工智能模型培训和部署,以及增强的安全/隐私集成.
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