ARTIFICIAL INTELLIGENCE DRIVEN CLINICAL DECISION SUPPORT SYSTEMS IN AYURVEDA: CURRENT EVIDENCE, CHALLENGES, AND FUTURE DIRECTION
TL;DR
An Explainable Artificial Intelligence Driven Clinical Decision Support System (XAI CDSS) is proposed that combines multimodal clinical data, evidence-based reasoning, physician validation, and continuous learning to strengthen evidence based, personalized Ayurvedic care.
Abstract
Background: Artificial intelligence (AI) has become a transformative force in healthcare, guiding diagnosis and treatment through machine learning, deep learning, natural language processing, and computer vision. While these tools have matured quickly in conventional medicine, their use in Ayurveda remains patchy and largely exploratory. Objective: This review summarizes current evidence on AI applications in Ayurveda, outlines the scientific and practical barriers limiting clinical adoption, and proposes an explainable AI based clinical decision support framework suited to Ayurvedic practice. Methods: A narrative review of literature on artificial intelligence, clinical decision support systems, digital health, biomedical informatics, and Ayurvedic medicine was conducted. Evidence relating to Prakriti assessment, digital Nadi analysis, tongue diagnosis, natural language processing, herbal drug discovery, disease prediction, and personalized treatment was synthesized, and current gaps, research priorities, and opportunities for explainable AI in Ayurvedic clinical workflows were critically examined. Results: AI applications in Ayurveda show encouraging results across constitutional assessment, digital diagnostics, text mining, herbal drug discovery, and personalized healthcare, but most studies remain proof of concept work limited by small datasets, inconsistent methods, minimal external validation, and little formal clinical evaluation. The absence of standardized electronic Ayurvedic health records, interoperable ontologies, multicentric annotated datasets, explainable algorithms, and prospective validation studies is the central bottleneck. To address this, we propose an Explainable Artificial Intelligence Driven Clinical Decision Support System (XAI CDSS) that combines multimodal clinical data, evidence-based reasoning, physician validation, and continuous learning. Conclusion: AI can meaningfully strengthen evidence based, personalized Ayurvedic care, but real progress depends on standardized digital infrastructure, explainable and trustworthy models, rigorous clinical validation, ethical governance, and genuine interdisciplinary collaboration. AI should function as a physician assistive technology that augments clinical expertise while preserving the holistic principles of Ayurveda.