WHO Roadmap on AI in Traditional Medicine
Kartavya Desk Staff
Syllabus: Health
Source: TOI
Context: The WHO released its first technical roadmap titled “Mapping the Application of AI in Traditional Medicine” on the use of Artificial Intelligence in traditional medicine, adopting India’s proposal under the GI-AI4H initiative.
• It also recognised India’s key digital initiatives like TKDL and Ayurgenomics in the global framework.
About WHO Roadmap on AI in Traditional Medicine:
• Definition & Aim: The roadmap titled “Mapping the application of AI in traditional medicine” provides a strategic guide for safe, ethical, and effective AI integration in traditional healthcare systems.
• India’s Role: India led the proposal through Ministry of AYUSH, emphasizing digitisation, personalised medicine, and global AI standards in Ayurveda, Siddha, Unani, and other practices.
• Global Relevance: First of its kind under WHO’s Global Initiative on AI for Health (GI-AI4H) with partner countries.
Status of Traditional Medicine Globally:
• Global Usage: Over 80% of the world’s population uses some form of traditional medicine (WHO).
• India’s Share: Home to over 500,000 AYUSH practitioners; strong push through National AYUSH Mission, TKDL, and collaborations with WHO Global Centre for Traditional Medicine (GCTM), Jamnagar.
• Market Size: Global traditional medicine market expected to cross $200 billion by 2030.
Need for AI in Traditional Medicine:
• Personalized Care: AI enables custom treatment by aligning Ayurveda’s prakriti with modern genomics (Ayurgenomics).
• Evidence Creation: AI can analyze large classical texts and clinical data to validate traditional practices.
• Cost-effective Tools: Chatbots, mobile diagnostics, and virtual support systems increase access, especially in rural areas.
• Global Acceptance: AI offers a standardized clinical language for integrating traditional systems in modern care.
• Data Management: AI simplifies classification of herbs, symptoms, and diagnostics from thousands of classical sources.
Challenges in AI-Enabled Traditional Medicine:
• Ethical Concerns: Risk of data bias, consent violation, and incorrect predictions using unverified datasets.
• Lack of Evidence Base: Many traditional therapies lack clinical trials or structured outcome data for AI training.
• Regulatory Ambiguity: No clear legal norms governing AI use in Ayurveda, Siddha, Unani, etc.
• Cultural Fragmentation: Variations between global traditional systems make interoperability difficult.
• Trust Issues: AI-generated advice may erode traditional practitioner-patient trust without adequate explainability.
Key Features of WHO’s AI Roadmap:
• Use Case Mapping: AI tools categorized into diagnostics, clinical support, text digitization, and public health.
• Governance Principles: Includes transparency, safety, explainability, accountability, and fairness in AI systems.
• Technical Enablers: Emphasis on interoperable data sets, skilled workforce, regulatory frameworks.
• Innovation Models: Encourages co-creation between AI engineers and traditional medicine practitioners.
• Country-Specific Examples: Recognizes India’s TKDL, Ayurgenomics, and planned AYUSH AI Chatbots.
Conclusion:
India’s leadership in combining ancient wisdom with cutting-edge AI has received global validation. WHO’s roadmap lays the foundation for a secure and inclusive AI transition in traditional medicine. The challenge now lies in balancing innovation with ethical safeguards for long-term global health integration.