Assessing Artificial Intelligence Solution Effectiveness: The Role of Pragmatic Trials

Mauricio F. Jin MD , Peter A. Noseworthy MD , Xiaoxi Yao PhD
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Abstract

The emergence of artificial intelligence (AI) and other digital solutions in health care has considerably altered the landscape of medical research and patient care. Rigorous evaluation in routine practice settings is fundamental to the ethical use of AI and consists of 3 stages of evaluations: technical performance, usability and acceptability, and health impact evaluation. Pragmatic trials often play a key role in the health impact evaluation. The current review introduces the concept of pragmatic trials, their role in AI evaluation, the challenges of conducting pragmatic trials, and strategies to mitigate the challenges. We also examined common designs used in pragmatic trials and highlighted examples of published or ongoing AI trials. As more health systems advance into learning health systems, where outcomes are continuously evaluated to refine processes and tools, pragmatic trials embedded into everyday practice, leveraging data and infrastructure from delivering health care, will be a critical part of the feedback cycle for learning and improvement.

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评估人工智能解决方案的有效性:实用性试验的作用
人工智能(AI)和其他数字解决方案在医疗保健领域的出现极大地改变了医学研究和患者护理的格局。在常规实践环境中进行严格评估是合乎伦理地使用人工智能的基础,评估包括三个阶段:技术性能、可用性和可接受性以及健康影响评估。实用性试验通常在健康影响评估中发挥关键作用。本综述介绍了实用性试验的概念、实用性试验在人工智能评估中的作用、开展实用性试验所面临的挑战以及应对挑战的策略。我们还研究了实用性试验中常用的设计,并重点介绍了已发表或正在进行的人工智能试验实例。随着越来越多的医疗系统向学习型医疗系统迈进,对结果进行持续评估以完善流程和工具,利用提供医疗保健的数据和基础设施将实用性试验嵌入日常实践中,这将成为学习和改进反馈循环的关键部分。
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来源期刊
Mayo Clinic Proceedings. Digital health
Mayo Clinic Proceedings. Digital health Medicine and Dentistry (General), Health Informatics, Public Health and Health Policy
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审稿时长
47 days
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