An Explainable AI Framework Integrating Traditional Chinese Medicine Knowledge Graphs and Digital Biomarkers for Personalized Edible Herbal Formula Recommendations in Chronic Disease Management

Abstract

Chronic diseases remain the leading cause of mortality and disability worldwide, demanding a shift from standardized treatment toward precision health strategies that account for inter-individual variability. Traditional Chinese Medicine (TCM) offers a holistic paradigm of syndrome differentiation and personalized herbal formulations that has been refined over millennia, yet its integration into modern clinical workflows is hindered by a lack of quantitative evidence, methodological standardization, and computational tools that can reconcile its conceptual depth with contemporary biomedical data. This paper presents a system-level framework for an explainable artificial intelligence (XAI) engine that combines structured TCM knowledge graphs with longitudinally acquired digital biomarkers to generate personalized edible herbal formula recommendations. We critically examine the architectural design choices, data governance models, interoperability requirements, fairness considerations, and sustainability challenges associated with deploying such a system at scale. The proposed framework adopts a layered architecture in which a dynamically curated TCM knowledge graph, populated with herb-compound-target relationships, syndrome associations, and safety constraints, interacts with a multi-modal biomarker ingestion pipeline that processes data from wearable sensors, continuous metabolic monitors, and self-reported outcomes. Explainability is achieved not through post-hoc approximation alone but through an inherently interpretable inference backbone that traces evidential paths across the knowledge graph and communicates therapeutic rationales aligned with TCM diagnostic principles. The discussion extends to regulatory alignment, the mitigation of cultural and demographic biases, and the long-term institutional and environmental costs of maintaining a living knowledge ecosystem. By articulating these cross-cutting concerns, the paper offers a blueprint for future translational research that bridges ancient medical wisdom with data-driven personalization in chronic disease management.