Medical School, University of Birmingham, Birmingham, UK.
Centre for Patient Reported Outcomes Research, Institute of Applied Health Research, University of Birmingham, Birmingham, UK; Birmingham Health Partners Centre for Regulatory Science and Innovation, University of Birmingham, Birmingham, UK; Data-Enabled Medical Technologies and Devices Hub, University of Birmingham, Birmingham, UK.
Lancet Digit Health. 2023 Mar;5(3):e160-e167. doi: 10.1016/S2589-7500(22)00249-7.
The extent to which patient-reported outcome measures (PROMs) are used in clinical trials for artificial intelligence (AI) technologies is unknown. In this systematic evaluation, we aim to establish how PROMs are being used to assess AI health technologies. We searched ClinicalTrials.gov for interventional trials registered from inception to Sept 20, 2022, and included trials that tested an AI health technology. We excluded observational studies, patient registries, and expanded access reports. We extracted data regarding the form, function, and intended use population of the AI health technology, in addition to the PROMs used and whether PROMs were incorporated as an input or output in the AI model. The search identified 2958 trials, of which 627 were included in the analysis. 152 (24%) of the included trials used one or more PROM, visual analogue scale, patient-reported experience measure, or usability measure as a trial endpoint. The type of AI health technologies used by these trials included AI-enabled smart devices, clinical decision support systems, and chatbots. The number of clinical trials of AI health technologies registered on ClinicalTrials.gov and the proportion of trials that used PROMs increased from registry inception to 2022. The most common clinical areas AI health technologies were designed for were digestive system health for non-PROM trials and musculoskeletal health (followed by mental and behavioural health) for PROM trials, with PROMs commonly used in clinical areas for which assessment of health-related quality of life and symptom burden is particularly important. Additionally, AI-enabled smart devices were the most common applications tested in trials that used at least one PROM. 24 trials tested AI models that captured PROM data as an input for the AI model. PROM use in clinical trials of AI health technologies falls behind PROM use in all clinical trials. Trial records having inadequate detail regarding the PROMs used or the type of AI health technology tested was a limitation of this systematic evaluation and might have contributed to inaccuracies in the data synthesised. Overall, the use of PROMs in the function and assessment of AI health technologies is not only possible, but is a powerful way of showing that, even in the most technologically advanced health-care systems, patients' perspectives remain central.
患者报告结局测量（PROMs）在人工智能（AI）技术的临床试验中应用的程度尚不清楚。在这项系统评价中，我们旨在确定 PROMs 用于评估 AI 健康技术的方式。我们在 ClinicalTrials.gov 上检索了从成立到 2022 年 9 月 20 日注册的干预性试验，并纳入了测试 AI 健康技术的试验。我们排除了观察性研究、患者登记处和扩大准入报告。我们提取了关于 AI 健康技术的形式、功能和预期使用人群的数据，以及使用的 PROMs，以及 PROMs 是否作为 AI 模型的输入或输出纳入。搜索确定了 2958 项试验，其中 627 项被纳入分析。包括的试验中，有 152 项（24%）使用了一种或多种 PROM、视觉模拟量表、患者报告体验测量或可用性测量作为试验终点。这些试验中使用的 AI 健康技术类型包括 AI 支持的智能设备、临床决策支持系统和聊天机器人。在 ClinicalTrials.gov 上注册的 AI 健康技术临床试验数量以及使用 PROMs 的试验比例从登记开始到 2022 年有所增加。非 PROM 试验中 AI 健康技术设计的最常见临床领域是消化系统健康，而 PROM 试验中最常见的是肌肉骨骼健康（其次是精神和行为健康），PROM 常用于健康相关生活质量和症状负担评估特别重要的临床领域。此外，智能设备是使用至少一种 PROM 的试验中最常见的测试应用。24 项试验测试了将 PROM 数据作为 AI 模型输入捕获的 AI 模型。在 AI 健康技术的临床试验中，PROM 的使用落后于所有临床试验。这项系统评价的一个局限性是试验记录中关于使用的 PROM 或测试的 AI 健康技术类型的细节不足，这可能导致数据综合的不准确。总的来说，PROM 在 AI 健康技术的功能和评估中的使用不仅是可能的，而且是展示即使在最先进的医疗保健系统中，患者的观点仍然是核心的有力方式。
