简化医疗软件开发与CARE生命周期和CARE代理:一个AI驱动的技术准备程度评估工具
Steven N Hart1, Patrick L Day2, Christopher A Garcia2
1Department of Laboratory Medicine and Pathology, Mayo Clinic, 200 1st St. SW, Rochester, MN, 55901, USA. hart.steven@mayo.edu.
BMC medical informatics and decision making
|July 8, 2025
概括
这项研究引入了一个新的框架和人工智能工具来评估医疗人工智能准备,简化开发和提高临床安全. 该系统有助于识别差距,减少手动审查,并加速部署安全有效的医疗AI技术.
科学领域:
- 医疗软件开发 医疗软件开发
- 临床人工智能 临床人工智能
- 机器学习系统 机器学习系统
背景情况:
- 医疗软件开发面临着监管,伦理和运营方面的障碍.
- 确保技术成熟度和临床安全对于AI/ML部署至关重要.
- 现有的方法缺乏临床AI准备的综合方法.
研究的目的:
- 介绍临床人工智能准备评估器 (CARE) 生命周期和代理.
- 简化医疗人工智能软件的技术准备水平评估.
- 为评估临床AI/ML系统成熟度提供一个框架.
主要方法:
- 代框架开发与利益相关者分析 (临床信息学,数据工程师,伦理学家,运营领导者).
- 使用检索增强生成的AI驱动工具.
- 通过合成用例 (糖尿病结果预测器) 进行评估,其中包含有关问题覆盖范围和审查时间的指标.
主要成果:
- CARE框架有效地解决了临床AI开发的复杂性.
- 人工智能工具在一个综合案例中发现了32.8%的未回答评估问题.
- 人类判断证实了这些实例中的19.4%的差异,表明减少手动审查和加快时间表的可能性.
结论:
- CARE生命周期和代理提供了一种强大的方法来评估医疗AI成熟度.
- 整合利益相关者的见解和AI评估可以提高安全性和有效性.
- 未来的工作旨在优化AI检索策略,并在各种临床应用中进行验证.
关键词:
人工智能驱动的评估是人工智能驱动的评估.技术评估中的自动化.护理代理人 护理代理人在CARE生命周期中,数据隐私 数据隐私地方部署 地方部署检索增强生成 (RAG) 是一个.自助服务工具自助服务工具利益相关者的人格.技术准备水平 (TRL) 是指技术准备水平.更多相关视频
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