人工智能支持的临床算法开发的战略治理:半自动临床算法开发框架的形成性评估
Sang Hyun Ahn1,2,3, Junhewk Kim4
1MoDoc AI Inc., Needham, MA, United States.
JMIR formative research
|March 12, 2026
概括
半自动临床算法开发 (S-ACAD) 框架平衡了人工智能速度与安全临床内容的专家监督. 这种人为循环的方法显示了有效,安全的儿科紧急指导开发的前景.
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:
- 医疗保健领导者正在努力平衡人工智能驱动的内容开发速度与传统方法的安全性.
- 人工智能自动化提供了效率,但有临床内容不准确的风险 (幻觉).
- 强大的治理对于将人工智能纳入医疗保健至关重要,同时确保患者安全.
研究的目的:
- 描述半自动临床算法开发 (S-ACAD) 框架的开发和评估.
- 评估用于儿科紧急指导的人在循环模型的可行性.
- 在人工智能辅助的临床内容创建中,平衡运营效率与严格的安全标准.
主要方法:
- 一个前性的,为期一天的概念验证案例研究,针对儿科发烧性发作.
- 一位医生专家使用了四阶段的工作流:人工智能数据收集,人工智能合成,人工智能训练和临床验证.
- 与完全自主临床算法开发 (F-ACAD) 系统进行基准测试.
主要成果:
- 在S-ACAD框架下,在约245分钟内产生了一个父可操作的算法,独立专家对其临床有效性进行了高度评价.
- 记录了19个人类专家干预,主要用于临床判断和安全审查.
- F-ACAD系统在68分钟内完成,但标记了17个问题,包括清晰度和护理标准问题.
结论:
- S-ACAD框架显示了人工智能辅助的临床内容开发中积极治理的潜力.
- 这种人-在-循环模型可以减少交付时间,同时保持安全防护措施.
- 为了通用性,需要对各种专家,主题和环境进行进一步的验证.
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