This is what a new WHO report on the use of AI for medical purposes says: it highlights the technology's transformative potential.

The World Health Organization (WHO), the International Telecommunication Union (ITU), and the World Intellectual Property Organization (WIPO) have highlighted the transformative potential of artificial intelligence (AI) for traditional medicine and have called for the development of regulatory frameworks that govern its application and respect the cultural diversity of Indigenous peoples and local communities.

These three organizations presented this Friday the technical report "Mapping the Application of Artificial Intelligence in Traditional Medicine," within the framework of the Global Initiative on AI for Health. This report offers a roadmap for harnessing this potential responsibly, while safeguarding cultural heritage and data sovereignty.

Traditional, complementary and integrative medicine (TCIM) is defined by the WHO as the body of knowledge, skills, and practices based on the theories, beliefs, and experiences of indigenous peoples across cultures, whether explicable or not, used in health care, prevention, diagnosis, and treatment of physical or mental illness.

The global health agency is aware of the use of herbal medicines, acupuncture, yoga, indigenous therapies, and other forms of traditional medicine in 170 countries, so these practices have already become a global phenomenon, with growing demand among the population.

"Our Global Initiative on AI for Health aims to help all countries benefit from AI solutions and ensure they are safe, effective, and ethical," explained Seizo Onoe, Director of the ITU Telecommunication Standardization Bureau.

The document outlines various uses of AI in the context of traditional medicine that are already being implemented around the world, including diagnosis and personalized care, drug development, management and planning of healthcare systems, and the preservation and promotion of traditional medical knowledge.

Specifically, it includes examples such as the use of AI-based diagnostics in Ayurvedic medicine, the combination of traditional Indian Ayurvedic medicine with genomics, the study of genes and their function; machine learning models that identify medicinal plants in countries like Ghana and South Africa; and the use of AI to analyze traditional medicinal compounds to treat blood disorders in South Korea.

The global market for traditional and complementary medicine is expected to reach nearly €513 million (US$600 billion) by 2025. In this context, the report highlights that AI could further accelerate its growth and impact on global health.

Despite the clear potential of AI in this area, the report highlights the need to develop regulatory frameworks, knowledge sharing, capacity building, data governance, and equity promotion to ensure the safe, ethical, and evidence-based integration of this new technology into traditional medicine.

In this regard, it urges countries to take measures to uphold Indigenous Data Sovereignty (IDSov) and ensure that AI development is governed by the principles of free, prior, and informed consent. As examples, it presents community-led data governance models from Canada, New Zealand, and Australia, and calls on governments to adopt laws that empower Indigenous peoples to control and benefit from their data.

"AI must not become a new frontier of exploitation," said WHO Assistant Director-General for Health Systems Yukiko Nakatani, who stressed the importance of ensuring not only the protection of Indigenous peoples and local communities, but also their active participation in shaping the future of AI in traditional medicine.

To achieve this, the report calls on stakeholders to invest in inclusive AI ecosystems that respect cultural diversity and IDSov, and to develop national policies and legal frameworks that explicitly address AI in traditional medicine.

It also calls for developing the capacity and digital literacy of traditional medicine practitioners and communities; establishing global standards for data quality, interoperability, and the ethical use of AI; and safeguarding traditional knowledge through AI-powered digital repositories and benefit-sharing models.