建立和实施一个负责任的人工智能框架:一年的审查
Ada H Tsoi1, Gary Gartner1, Steven W Cotten2
1ISD-EADS, UNC Health, Morrisville, NC 27560, United States.
Journal of the American Medical Informatics Association : JAMIA
|September 19, 2025
概括
在医疗保健中实施负责任的人工智能 (RAI) 框架显示了公平性和供应商透明度的挑战. 该框架需要资源和合作来有效评估AI工具的可信度.
科学领域:
- 医疗保健人工智能的人工智能
- 负责的人工智能实施
- 临床信息学 临床信息学
背景情况:
- 人工智能 (AI) 越来越多地融入医疗保健,因此需要强大的框架来负责任地实施.
- 确保医疗保健中的公平,透明,问责和可信度的人工智能对患者安全和道德实践至关重要.
研究的目的:
- 突出在医疗保健环境中实施新型负责任的人工智能 (RAI) 框架的成功和挑战.
- 强调RAI框架运行所需的跨学科医疗保健专业知识.
主要方法:
- 在UNC健康开发了一个21个问题RAI摄入调查,以评估AI解决方案.
- 调整调查问题与公平,透明,问责和可信度的机构目标.
- 由临床,分析和运营专家组成的多学科团队对AI解决方案的评估.
主要成果:
- 使用RAI框架进行了12次AI解决方案评估.
- 在评估的人工智能解决方案中发现了较低的公平性得分.
- 83%的人工智能解决方案获得了有条件批准,等待进一步审查或减轻.
结论:
- 关键的学习包括对代表性培训数据集的关键需求和供应商模型的系统评估.
- 遇到的挑战包括不频繁的人口分层分析,有限的供应商透明度和依赖志愿者参与.
- 该RAI框架为医疗保健AI评估提供了路线图,但面临着诸如资源限制和供应商合作等障碍;未来的代应考虑基于风险的分层评估和成员参与可扩展性.
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