The HALO Model: A Learning Health System Framework for Artificial Intelligence

Introduction: Artificial intelligence is increasingly embedded in healthcare delivery, yet existing Learning Health System (LHS) models do not fully account for the lifecycle management and continuous assurance requirements of AI systems. This gap limits health systems' ability to safely and sustainably integrate AI as a learning component of care. Methods: We conducted a conceptual system modeling investigation grounded in LHS theory and contemporary AI governance frameworks. Through structured theoretical integration, we aligned the classical LHS learning cycle with an action-oriented AI lifecycle and five continuous assurance dimensions, developing a unified framework to support operational implementation within health systems. Results: The resulting Health AI Learning and Oversight (HALO) model specifies how AI functions as a dynamic knowledge artifact within an LHS. Application of the model illustrates how integrating lifecycle stages and continuous assurance instantiates iterative learning loops, enables adaptive governance, and supports operational lifecycle management, including ongoing monitoring of performance, safety, equity, transparency, and security across clinical environments. Conclusions: By extending LHS theory to incorporate AI lifecycle and assurance requirements explicitly, the HALO framework operationalizes continuous learning and oversight for AI-enabled health systems. This model provides a foundation for designing, governing, and sustaining responsible and adaptive AI deployment as healthcare environments evolve.