Integration of Artificial Intelligence and Machine Learning into African Nutrition: Missed Opportunities for Public Health

The African continent faces a myriad of problems affecting its food and nutrition systems, including the double burden of malnutrition, a complex coexistence of persistent undernutrition and micronutrient deficiencies and increasing obesity and diet-related non-communicable diseases, compounded by climate change. While artificial intelligence (AI) and machine learning (ML) are rapidly transforming healthcare and medicine, their integration into global nutrition and food systems trails significantly. In this Viewpoint, we argue that Africa cannot adequately address these systematic nutrition and health challenges without harnessing the strength of AI and machine learning-based tools, technologies and approaches, concurrently with proven, effective nutrition interventions. Current barriers to integrating AI in Africa include limited financial and technological resources, a lack of prospective data and sparse dietary data, limited analytical capacity, weak AI governance, and poor private sector engagement in public-good AI. High-income countries have prioritized precision nutrition, individualized risk prediction, and individual diet assessment tools. We argue that in Africa and other low- and middle-income countries, AI can be leveraged to enhance critical functions of nutrition systems, including service delivery, behavior change communication with chatbots and local language tools, nutrition surveillance, and evidence-informed decision-making, among other roles. To unlock this potential, African countries must invest in data systems and digital infrastructure, capacity building, research and innovation ecosystems, robust governance and ethics, and equitable frameworks.  Ultimately, all these require global and local collaboration and funding to ensure the continent reaches its full potential.