Mapping the Application of Artificial Intelligence in Traditional Medicine

Syllabus: GS2/ Health

In Context

• India, with its rich Ayush systems (Ayurveda, Siddha, Unani, Sowa Rigpa, and Homoeopathy), has recently been recognized by the World Health Organization (WHO) for pioneering the integration of Artificial Intelligence (AI) into these systems.

What is Traditional Medicine?

• Traditional medicine encompasses the knowledge, skills, and practices based on indigenous theories and experiences, often involving plant, animal, and mineral-based remedies, spiritual therapies, and manual techniques to maintain health or treat illness. 

Role of AI in Traditional Medicine

• Enhancing Diagnostics: AI-powered systems combine traditional diagnostic methods (pulse reading, tongue analysis, Prakriti assessment) with machine learning and deep neural networks, improving accuracy and enabling personalized care.
• Ayurgenomics: AI merges genomic data with Ayurvedic principles to identify disease risk markers and tailor health recommendations, advancing personalized medicine.
• Drug Discovery & Validation: AI analyzes the molecular basis of herbal formulations, supports drug repurposing, and aids in comparative studies across traditional systems.
• Knowledge Preservation: AI tools facilitate the cataloguing and semantic analysis of ancient texts, making therapeutic knowledge more accessible and protecting against biopiracy.
• Health System Management: AI-enabled digital records and hospital management systems optimize data collection, patient care, and research in traditional medicine.

India’s Initiatives to Facilitate AI in Traditional Medicine

• Ayush Grid: A digital health platform underpinning citizen-centric initiatives and supporting the digital transformation of Ayush systems.
• AI-Driven Portals: Platforms like the SAHI portal, NAMASTE portal, and Ayush Research Portal enable online consultations, research, and digital literacy among practitioners.
• Traditional Knowledge Digital Library (TKDL): A globally recognized digital repository preserving and protecting India’s indigenous medical heritage.
• Policy Leadership: India proposed and contributed to WHO’s first global roadmap for AI in traditional medicine, reflecting its commitment to “AI for All”.

Challenges

• Data Quality & Standardization: Lack of large, reliable, and standardized datasets in traditional medicine hampers effective AI training and validation.
• Digital Gaps: Limited digital infrastructure and low digital literacy among practitioners, especially in rural areas, restrict AI adoption.
• Biopiracy & Data Sovereignty: Risks of misappropriation of indigenous knowledge and resources without consent.
  • Ensuring data privacy, security, and ethical use of AI in sensitive health contexts.
• Human Touch: AI cannot fully replicate the nuanced, empathetic care provided by traditional practitioners

Way Ahead

• Strengthen Data Governance: Build robust frameworks to ensure data privacy, protect indigenous rights, and standardize data collection.
• Capacity Building: Invest in digital literacy and infrastructure to bridge the digital divide.
• Global Collaboration: Foster international cooperation for research, policy, and ethical standards.
• Evidence-Based Integration: Continue validating traditional practices through scientific research and AI, ensuring safe, effective, and accessible healthcare for all.

Source: PIB