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In the evolving landscape of artificial intelligence (AI) applications within healthcare, device-free methodologies offer promising avenues for non-invasive patient monitoring, particularly in post-treatment recovery phases. This conceptual manuscript introduces a novel architectural framework, termed the mobility outcome inference network  (MOIN), designed to infer clinical outcomes from mobility-derived data without reliance on wearable or implanted devices. Drawing upon ambient sensing technologies and AI-driven analytics, MOIN integrates multi-modal data streams from environmental sensors to derive inferences on patient recovery trajectories. The architecture emphasizes interoperability with electronic health records (EHRs), decision support pipelines, and governance mechanisms to ensure ethical deployment and continuous monitoring. Key components include layered data orchestration for real-time mobility pattern analysis, feedback loops for adaptive inference refinement, and theoretical models for risk assessment in outcome predictions. By synthesizing recent literature on clinical AI systems, healthcare analytics infrastructures, and interoperability frameworks, this work delineates a blueprint for scalable, device-independent recovery assessment. Potential implications span enhanced clinical workflows, reduced patient burden, and improved equity in healthcare delivery, while addressing challenges such as data privacy and algorithmic fairness. This conceptual design prioritizes theoretical robustness over empirical validation, proposing interpretive formulas for decision confidence and governance load to guide future implementations in diverse clinical settings. Ultimately, MOIN aims to advance AI governance in mobility-based analytics, fostering resilient infrastructures for outcome inference in resource-constrained environments.