Machine learning (ML) and artificial intelligence (AI) algorithms have the potential to derive insights from clinical data and improve patient outcomes. However, these highly complex systems are sensitive to changes in the environment and liable to performance decay. Even after their successful integration into clinical practice, ML/AI algorithms should be continuously monitored and updated to ensure their long-term safety and effectiveness. To bring AI into maturity in clinical care, we advocate for the creation of hospital units responsible for quality assurance and improvement of these algorithms, which we refer to as “AI-QI” units. We discuss how tools that have long been used in hospital quality assurance and quality improvement can be adapted to monitor static ML algorithms. On the other hand, procedures for continual model updating are still nascent. We highlight key considerations when choosing between existing methods and opportunities for methodological innovation.

机器学习 (ML) 和人工智能 (AI) 算法有可能从临床数据中获得洞察力并改善患者的治疗效果。然而，这些高度复杂的系统对环境的变化很敏感，并且容易出现性能衰减。即使在成功融入临床实践之后，也应持续监控和更新 ML/AI 算法，以确保其长期安全性和有效性。为了使人工智能在临床护理中走向成熟，我们提倡创建负责质量保证和改进这些算法的医院单位，我们将其称为“AI-QI”单位。我们讨论了长期用于医院质量保证和质量改进的工具如何适用于监控静态 ML 算法。另一方面，持续模型更新的程序仍处于初期阶段。我们强调在现有方法和方法创新机会之间进行选择时的关键考虑因素。