Online artificial intelligence (AI) algorithms are an important component of digital health interventions. These online algorithms are designed to continually learn and improve their performance as streaming data is collected on individuals. Deploying online AI presents a key challenge: balancing adaptability of online AI with reproducibility. Online AI in digital interventions is a rapidly evolving area, driven by advances in algorithms, sensors, software, and devices. Digital health intervention development and deployment is a continuous process, where implementation - including the AI decision-making algorithm - is interspersed with cycles of re-development and optimization. Each deployment informs the next, making iterative deployment a defining characteristic of this field. This iterative nature underscores the importance of reproducibility: data collected across deployments must be accurately stored to have scientific utility, algorithm behavior must be auditable, and results must be comparable over time to facilitate scientific discovery and trustworthy refinement. This paper proposes a reproducible scientific workflow for developing, deploying, and analyzing online AI decision-making algorithms in digital health interventions. Grounded in practical experience from multiple real-world deployments, this workflow addresses key challenges to reproducibility across all phases of the online AI algorithm development life-cycle.

翻译：在线人工智能算法是数字健康干预措施的重要组成部分。这些在线算法旨在随着个体流数据的收集而持续学习并提升性能。部署在线人工智能面临一个关键挑战：在算法的适应性与可复现性之间取得平衡。数字干预中的在线人工智能是一个快速发展的领域，其驱动力来自算法、传感器、软件和设备的进步。数字健康干预措施的开发与部署是一个持续的过程，其中实施（包括人工智能决策算法）与再开发和优化的周期交织进行。每次部署都为下一次提供信息，使得迭代部署成为该领域的显著特征。这种迭代特性凸显了可复现性的重要性：跨部署收集的数据必须被准确存储以具备科学价值，算法行为必须可审计，结果必须随时间可比，以促进科学发现和可信的优化。本文提出了一种可复现的科学工作流，用于在数字健康干预中开发、部署和分析在线人工智能决策算法。该工作流基于多个实际部署的实践经验，解决了在线人工智能算法开发生命周期各阶段中可复现性的关键挑战。