A Prescriptive Validation Framework for a Scalable Multi-Layer AI Adoption Model in 6P Medicine

The integration of artificial intelligence (AI) into healthcare systems is central to the realization of 6P Medicine that emphasizes Predictive, Preventive, Personalized, Participatory, Precision-oriented, and Public-centered care. While several conceptual AI adoption models have been proposed, few provide prescriptive guidance for real-world validation across technical, sociotechnical, and ethical dimensions. This paper introduces a comprehensive validation framework for the Scalable Multi-Layer AI Adoption Model for 6P Medicine. The framework aligns architectural prerequisites, regulatory governance, and continuous lifecycle monitoring with the six interdependent layers of the model. Validation is addressed across data integrity, model robustness, clinical efficacy, human-AI collaboration, scalability, and ethical governance, drawing on FDA Good Machine Learning Practice (GMLP) principles and WHO regulatory considerations. The resulting framework intends to support continuous, real-world validation, positioning AI as a trustworthy, scalable, and ethically governed enabler of 6P Medicine.