Mobile health has the potential to revolutionize health care delivery and patient engagement. In this work, we discuss how integrating Artificial Intelligence into digital health applications-focused on supply chain, patient management, and capacity building, among other use cases-can improve the health system and public health performance. We present an Artificial Intelligence and Reinforcement Learning platform that allows the delivery of adaptive interventions whose impact can be optimized through experimentation and real-time monitoring. The system can integrate multiple data sources and digital health applications. The flexibility of this platform to connect to various mobile health applications and digital devices and send personalized recommendations based on past data and predictions can significantly improve the impact of digital tools on health system outcomes. The potential for resource-poor settings, where the impact of this approach on health outcomes could be more decisive, is discussed specifically. This framework is, however, similarly applicable to improving efficiency in health systems where scarcity is not an issue.

本研究解决了数字健康应用中人工智能整合不足的问题，提出了一种基于人工智能和强化学习的平台，以实现针对供应链、患者管理和能力建设的适应性干预。研究表明，该平台通过实时监测和实验优化的个性化推荐，能够显著提升健康系统的绩效，尤其在资源匮乏的环境中具有更为显著的潜在影响。

健康领域的数字化转型：人工智能如何提升健康系统表现