WHO urges a shift from extractive AI practices to participatory, culturally anchored & ethically sound implementations that center the rights and knowledge of Indigenous Peoples and local communities.

Mapping the Application of Artificial Intelligence in Traditional Medicine

Based on the WHO-ITU Technical Brief (2025)

by ChatGPT-4o

Introduction

The 2025 technical brief Mapping the Application of Artificial Intelligence in Traditional Medicine, jointly issued by the World Health Organization (WHO) and the International Telecommunication Union (ITU), provides a comprehensive overview of how artificial intelligence (AI) is beginning to intersect with the domain of Traditional Medicine (TM). As digital technologies reshape health systems worldwide, this document explores the opportunities, risks, and regulatory needs for responsibly integrating AI into TM practices, particularly in contexts where Indigenous knowledge, biodiversity, and cultural heritage are deeply intertwined with health and healing.

Key Findings and Insights

1. Growing Relevance of TM and the Promise of AI

Traditional Medicine—encompassing systems like Ayurveda, Traditional Chinese Medicine (TCM), Unani, and Indigenous healing practices—remains essential to billions of people globally. It plays a vital role in achieving Universal Health Coverage (UHC), especially in low- and middle-income countries. The brief estimates the TM market may reach $583 billion by 2025, underscoring its economic significance.

AI offers immense potential to amplify the reach, safety, and efficacy of TM through personalized diagnosis, clinical decision support, drug discovery, and preservation of traditional knowledge. Projects like India’s Ayurgenomics and South Africa’s Rooibos Genomics Programme exemplify this convergence of ancient wisdom with frontier science.

2. Use Cases Across the Health Spectrum

The brief highlights diverse AI use cases:

• Clinical Diagnosis & Prediction: Machine learning (ML) tools are enhancing pulse diagnosis and body-type assessments in Ayurveda and TCM.

• Drug Discovery & Genomics: AI is aiding in identifying active compounds, optimizing herbal formulations, and exploring herb-drug interactions.

• Clinical Trial Design: AI tools help refine participant selection, dosage optimization, and outcome tracking in TM research.

• Health System Management: AI-assisted OCR and EHR systems are digitizing TM patient records and enabling disease trend analysis.

• Knowledge Preservation: Repositories like India’s TKDL and PAHO’s Virtual Health Library use AI for cataloguing, indexing, and accessibility.

3. Ethical and Regulatory Gaps

Despite progress, TM is often excluded from national and global AI regulatory frameworks. Only 36% of WHO Member States have AI initiatives, and even fewer include TM. This fragmentation raises significant concerns about exploitation, biopiracy, and data sovereignty.

Furthermore, the application of AI in TM introduces new risks:

• Biopiracy: Unlicensed use of traditional knowledge by commercial AI models could marginalize Indigenous Peoples and local communities (IPLCs).

• Data Gaps and Bias: TM lacks standardized datasets and interoperable formats, raising concerns about fairness, bias, and misuse in AI applications.

• Digital Divide: Poor digital infrastructure and low AI literacy in TM-dependent regions could worsen health disparities.

Notable Initiatives and Models

The brief showcases several innovative programs:

• Terrastories (Brazil, US, Colombia): Uses AI to map oral histories and healing practices of Indigenous communities, with granular data access rights.

• Bridge2AI (USA): Integrates AI with integrative health for research and personalization.

• Our Data Indigenous (Canada): A community-led data governance platform applying Indigenous Data Sovereignty principles.

• Māori Data Governance Model (New Zealand): Ensures self-determination in health data use.

These models emphasize ethics, participatory design, and respect for FPIC (Free, Prior and Informed Consent), setting a foundation for ethical AI-TM integration.

Recommendations from the Brief

A. Policy and Governance

• Adapt existing WHO AI frameworks to reflect TM’s specific needs.

• Incorporate TM-focused AI risks into global health surveillance and regulation.

• Create benchmarking systems and evaluation metrics specific to TM-AI use cases.

B. Ethical AI Design and Development

• Ensure cultural context, linguistic accessibility, and explainability in AI models.

• Promote co-creation with TM users and Indigenous communities.

• Build AI tools that are transparent, auditable, and localized.

C. Empowerment and Capacity Building

• Invest in AI literacy for TM practitioners, regulators, and Indigenous communities.

• Support cross-disciplinary working groups (e.g., IP, ethics, R&D).

• Establish “communities of practice” and digital health ecosystems with TM experts.

D. Data Governance and Sovereignty

• Develop inclusive data governance policies guided by CARE and FAIR principles.

• Advance open-access repositories co-developed with IPLCs.

• Ensure that AI training data include diverse, validated TM sources to avoid bias.

E. Global Collaboration

• Harmonize TM terminologies and data standards across AI systems.

• Use platforms like WHO’s Global Traditional Medicine Library and ITU’s FG-AI4H to align global efforts and facilitate South-South collaboration.

Conclusion

This brief is a significant step toward responsibly unlocking AI’s potential in Traditional Medicine. It urges a shift from extractive AI practices to participatory, culturally anchored, and ethically sound implementations that center the rights and knowledge of Indigenous Peoples and local communities. As digital health accelerates, the equitable integration of AI into TM systems could not only enhance healthcare access and outcomes but also restore dignity, value, and recognition to ancient healing traditions. However, without urgent action, AI risks becoming yet another vehicle of erasure and inequality.

Final Thoughts

By foregrounding the cultural, legal, and epistemological nuances of TM, the WHO-ITU brief avoids the technocratic myopia often seen in digital health strategies. It invites governments, developers, and TM practitioners to co-author a future where AI respects tradition, upholds equity, and promotes health sovereignty.

This is not just about deploying new technologies—it is about preserving old wisdom, ethically transforming healthcare, and democratizing innovation.