This course aims to equip participants with foundational knowledge and hands-on tools to design, implement, and scale artificial intelligence (AI) and digital health solutions and programs that are context-specific and responsive to the unique needs of low-resource settings. Spanning four days, the course will examine real-world challenges and opportunities in deploying AI and digital health interventions in environments marked by limited infrastructure, workforce shortages, and fragmented data and health systems. Participants will explore approaches for assessing local priorities, planning inclusive, responsible and ethically grounded interventions, and developing AI and digital health programs that are not only scalable and sustainable but also adaptive to local capacity constraints and system-level realities.

After the successful completion of the course, participants will be able to answer the following questions:

• What are the foundational concepts of digital health and artificial intelligence (AI), and why are they particularly relevant in low-resource settings?
• How can the local context be effectively assessed to evaluate readiness for adopting digital health and AI-based solutions and programs?
• What are the essential steps to designing and implementing context-specific digital health and AI solutions and programs that address local priorities and challenges?
• How can developed solutions be designed to ensure they are ethical, inclusive, and aligned with the principles of responsible innovation?
• What strategies can be employed to promote the long-term scalability, sustainability, and impact of AI and digital health initiatives in low-resource settings?

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