1. Are system requirements defined with local realities in mind?
AI for health can falter when technical requirements are set externally, without input from the communities that will use it. Ensure that system requirements reflect local infrastructure, workflow constraints, and cultural context. Makerere’s team recognized that many rural clinics lacked internet and stable power, prompting the development of offline detection tools that run on smartphones.
2. Are communities actively involved in shaping the solution?
AI for health must be co-created with end-users, not imposed as a pre-packaged solution. Makerere partnered with local health facilities and practitioners from the outset, ensuring the system addressed real clinical needs, such as parasitemia quantification, rather than theoretical capabilities. Early community engagement aligns AI solutions with practical priorities and builds trust.
3. Is the data ethically collected and locally relevant?
High-quality datasets are essential, but imported or generic data can produce biased or ineffective models. The Makerere team built their own dataset through ethical and collaborative processes, obtaining consent, anonymizing data, and working closely with local clinics. Ask whether your AI for health relies on representative, ethically sourced data reflecting the population it is meant to serve.
4. Does the AI empower healthcare workers instead of replacing them?
AI for health should augment human expertise, not substitute it. Makerere’s AI tool provides decision support, speeding up diagnosis while keeping final judgment in the hands of skilled technicians. Before deployment, ensure your system maintains explainability, human oversight, and professional agency, reducing the risk of displacing local knowledge.
5. Are ethical and governance challenges anticipated?
Deploying AI for health involves navigating privacy, consent, and bureaucratic hurdles. Makerere’s team engaged proactively with ethical frameworks, data governance, and local authorities, ensuring compliance with regulations while protecting patient rights. Ask whether your deployment has robust ethical protocols, transparent governance, and safeguards for community trust.
AI for health in Africa is about equity, human rights, and sustainable impact. By asking these five questions, organizations can design AI for health systems that address real needs, respect local expertise, operate in constrained environments, and uphold ethical standards. The Makerere AI Health Lab demonstrates that when AI for health is thoughtfully designed with communities at the center, it can transform healthcare access, improve outcomes, and empower local practitioners, creating technology that truly serves the people it is meant to help.