University Hospitals of the KU Leuven, Leuven, Belgium.
Palindromo Consulting, Leuven, Belgium.
Phys Med. 2021 Mar;83:257-263. doi: 10.1016/j.ejmp.2021.04.006. Epub 2021 May 10.
In this study, we propose a framework to help the MPE take up a unique and important role at the introduction of AI solutions in clinical practice, and more in particular at procurement, acceptance, commissioning and QA.
The steps for the introduction of Medical Radiological Equipment in a hospital setting were extrapolated to AI tools. Literature review and in-house experience was added to prepare similar, yet dedicated test methods.
Procurement starts from the clinical cases to be solved and is usually a complex process with many stakeholders and possibly many candidate AI solutions. Specific KPIs and metrics need to be defined. Acceptance testing follows, to verify the installation and test for critical exams. Commissioning should test the suitability of the AI tool for the intended use in the local institution. Results may be predicted from peer reviewed papers that treat representative populations. If not available, local data sets can be prepared to assess the KPIs, or 'virtual clinical trials' could be used to create large, simulated test data sets. Quality assurance must be performed periodically to verify if KPIs are stable, especially if the software is upscaled or upgraded, and as soon as self-learning AI tools would enter the medical practice.
MPEs are well placed to bridge between manufacturer and medical team and help from procurement up to reporting to the management board. More work is needed to establish consolidated test protocols.
在这项研究中，我们提出了一个框架，旨在帮助医疗设备性能评估师（MPE）在人工智能解决方案引入临床实践中发挥独特而重要的作用，特别是在采购、验收、调试和质量保证（QA）方面。
将医院环境中引入医疗放射设备的步骤推断应用于人工智能工具。通过文献回顾和内部经验，我们制定了类似但专门的测试方法。
采购始于要解决的临床案例，通常是一个涉及众多利益相关者且可能存在多个候选人工智能解决方案的复杂过程。需要定义特定的关键绩效指标（KPI）和度量标准。验收测试紧随其后，用于验证安装并测试关键检查。调试应测试人工智能工具在本地机构中的预期用途的适用性。结果可以从针对代表性人群的同行评审论文中预测得出。如果没有可用的本地数据集，可以准备这些数据集来评估 KPI，或者使用“虚拟临床试验”来创建大型模拟测试数据集。必须定期进行质量保证，以验证 KPI 是否稳定，特别是在软件扩大规模或升级时，以及当自我学习的人工智能工具进入医疗实践时。
医疗设备性能评估师处于制造商和医疗团队之间的桥梁位置，有助于从采购到向管理委员会报告。需要做更多的工作来建立统一的测试协议。
