AyurXAI: Towards an Explainable Intelligence framework for P4 medicine

ABSTRACT: Personalized, preventive, predictive, and participatory (P4) medicine requires healthcare models that are simultaneously data-driven and interpretable. Current Artificial Intelligence (AI) systems excel in predictive accuracy across multimodal biomedical datasets, yet their outputs often lack interpretability and contextual reasoning, limiting clinical trust and adoption. Ayurveda, by contrast, is an integrative health system that emphasizes individual variability, causal inference, and ecological adaptation. We propose AyurXAI, a conceptual framework that integrates Ayurveda’s interpretive scaffolds with the computational capabilities of Explainable AI (XAI), thereby envisioning healthcare models that are not only predictive but also interpretable, individualized, and participatory. AyurXAI is structured around layered components – data, knowledge, reasoning, integration, and application – each informed by Ayurvedic developmental logic and AI methods. This perspective outlines how AyurXAI can address challenges of sparse data, generalizability, and clinical trust, and discusses its potential alignment with the four pillars of P4 medicine. Rather than a completed system, AyurXAI is advanced as a roadmap for future interdisciplinary research bridging traditional reasoning systems and modern computational intelligence. This integrative framework provides a roadmap for scalable, evidence-based integrative medicine and explainable digital health systems.