Page 57 - AI Standards for Global Impact: From Governance to Action

AI Standards for Global Impact: From Governance to Action

• RevolutionAIze shared a public health tool from India that uses AI to track and address
childhood malnutrition. The tool analyses existing WHO standards and integrates with
government healthcare applications.
• Uppsala University, Sweden and University of Helsinki, Finland presented an AI-based
point-of-care diagnostics, which have also been applied in East Africa for cancer screening Part 2: Thematic AI
and infectious disease diagnostics using mobile microscopy and cloud-based AI systems.
• WHO Eastern Mediterranean Regional Office shared the AI-powered All Hazard Information
Management toolkit, which speeds up outbreak decision-making and reduces document
preparation time.

The following key takeaways were seen as important factors for success:

• AI tools must be localized, culturally appropriate, and usable offline to ensure equity.
• Partnerships with governments, non-governmental organizations (NGOs), and academia
are critical for scale and sustainability.
• AI solutions should be designed for low-resource settings and integrated into existing
systems.

The following follow- up action areas were identified:

a) Develop standards for AI validation and contextual adaptation.
b) Support open, modular platforms for AI deployment in public health.
c) Advocate for equitable access and sustainability of AI tools.
d) Evolve a global experts network and develop partnerships for responsible use of AI.
e) Encourage data governance frameworks that support local ownership and privacy.

8�4 IP management to enable AI implementation for healthcare
innovation and access

This session, hosted by WIPO’s External Relations and Global Challenges Divisions, explored
how private innovators and governments can effectively leverage IP to ensure AI health solutions
reach markets and expand healthcare accessibility in low- and middle-income (LMICs) countries.

The following main points were highlighted when it comes to IP management:
• Africa needs to establish autonomous AI legal frameworks for healthcare, prioritizing
people-centred, ethical regulation amid fragmented governance. IP can incentivize
innovation but must be balanced with access and equity, especially in LMICs.
• Copyright management was highlighted as central to deploying AI in health, emphasizing
that fair use, transformative use, and derivative works are key legal concepts in AI.
• The industry uses AI in drug discovery and clinical trials but often keeps tools proprietary
due to data privacy. IP frameworks must align with AI’s technical realities for cross-
boundary functionality.

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