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从设计到完成:人工智能改变临床研究

Kanika Vats¹, Mohammad Mazhar Alam²

¹Department of Research and Innovation, Emirates Classification Society (TASNEEF), Abu Dhabi, ARE.

|November 20, 2025

概括

人工智能 (AI) 和机器学习 (ML) 通过优化患者招募,试验设计和数据分析,可以显著改善临床试验. 负责任的采用,解决诸如偏见和隐私等挑战,是提高试验效率和患者结果的关键.

科学领域:

临床研究方法论临床研究方法论
医学中的人工智能.
生物医学数据科学是生物医学数据科学.

背景情况:

传统的临床试验面临重大挑战,包括时间缓慢和高成本.
人工智能 (AI) 和机器学习 (ML) 已经成为临床研究生命周期中的变革性技术.
人工智能/ML集成有可能彻底改变研究设计,执行和分析.

研究的目的:

探索AI/ML为增强临床研究各个阶段所提供的机会.
确定和讨论与临床试验中AI/ML实施相关的关键挑战和伦理考虑.
为在临床研究中负责任地采用AI/ML提出解决方案和路线图.

主要方法:

对临床研究中AI/ML应用的当前文献进行审查和综合.
探索AI/ML在患者招募,试验设计,坚持,保留,安全监测和数据分析中的作用.
识别诸如选择偏见,数据隐私,伦理问题和监管合规等挑战.

主要成果:

人工智能/ML可以增强患者招募,优化试验设计,改善患者的坚持和保留,加强安全监测,并实现先进的数据分析.
关键的挑战包括选择偏见,隐私问题,道德考虑,监管障碍和数据漂移.
将人工智能见解与人类监督相结合的混合方法对于负责任的部署至关重要.

结论:

对AI/ML工具的负责任的部署和严格的验证对于临床研究至关重要.
积极应对挑战可以确保道德完整性,监管一致性以及对人工智能驱动的临床试验的信任.
人工智能/ML有可能显著提高试验效率,患者的治疗结果,并加快新疗法的开发.

关键词:

人工智能 (ai) 是一种人工智能.临床研究是临床研究.临床试验临床试验临床试验临床试验临床试验数据监测数据的监控.数字健康数字健康机器学习是机器学习.参与者招聘参与者招聘安全监控安全监控安全监控试验设计试验设计.试验的效率试验的效率.

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