基于AI/ML的策略,以提高随机临床试验中的公平,多样性和包容性
Shashidar Reddy Abbidi1, Debashree Sinha2
1, Charlotte, NC, USA. asreddy.cdm@gmail.com.
Trials
|February 12, 2026
概括
本研究介绍了一个AI/ML框架,以在临床试验中嵌入公平,多样性和包容性 (EDI). 它解决了代表人数不足的问题,以提高对所有患者的医学研究的有效性和公平性.
科学领域:
- 临床试验 临床试验
- 健康 公平 卫生 公平
- 人工智能在医学中的应用
背景情况:
- 随机临床试验 (RCT) 对于医疗治疗评估至关重要,但主要的人口统计数据的代表性不足.
- 代表性差距 (女性,老年人,少数群体,社会经济弱势群体) 限制了概括性,并可能加剧健康差异.
- 现有的试验方法难以确保不同患者群体的包容性和公平性.
研究的目的:
- 提出一个概念框架,在整个临床试验生命周期中整合公平,多样性和包容性 (EDI).
- 利用人工智能 (AI) 和机器学习 (ML) 来解决代表性差距,提高临床研究中的公平性.
- 通过先进的技术和方法方法,使EDI在临床试验中发挥作用.
主要方法:
- 开发了一个基于AI/ML的框架,整合了包容性设计的预测建模.
- 整合了适应性试验设计和实时多样性监测能力.
- 包括持续偏见检测机制,并为负责任的AI部署提供道德和法律保障.
主要成果:
- 分析证实,目前的RCT中的人口比例显著不足,突显了干预的必要性.
- 拟议的AI/ML框架使用预测建模,自适应设计和偏差监测实现EDI的运行.
- 证明了在临床试验中增强科学有效性和伦理完整性的途径.
结论:
- 通过AI/ML框架嵌入EDI对于提高医学证据可信度至关重要.
- 该框架提供了一种系统的方法来解决临床研究中持续存在的代表性差距.
- 这项工作为研究人员提供了一个结构化的途径,以进行更加公平和科学严格的临床试验.
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