1. Localization will be critical to the effective and ethical use of AI in African health care settings. Many of the AI tools we are familiar with today, such as ChatGPT and Google’s Gemini, are large language models (LLMs). They are trained using huge amounts of data to be able to provide intelligent and creative responses to prompts, like a human. Given that these models pick up information from the environment where they have been primarily used and trained, you can’t just assume they will work in any context. Existing LLMs developed to give medical advice to individuals or provide diagnostic assistance to physicians are trained using data sources that reflect and bake in the biases of WEIRD (Western, educated, industrialized, rich, and democratic) societies. For LLMs to be useful for community health workers in African countries, they need to be trained based on an understanding of local medical practices and vernacular.
Health care service provision can vary profoundly from country to country due to differences in burdens of disease, cultures, and local enabling environments. To think we can drag and drop a solution from a high-income setting into a low-resource environment is at best naive, and at worst negligent. To get the best results from AI, we have to think about developing LLMs for the many, not the (privileged) few, and start with intentional engagement of end users on day one of the product development cycle.
2. African markets and governments need to invest in their digital (enabling) infrastructure if they are going to benefit from AI. We are reaching a breakthrough moment for AI as the practical barriers to implementation rapidly get broken down. For example, advancements in physical infrastructure, such as solar energy and satellite internet access, are making it more feasible to digitally enable health facilities at the last mile. However, there are many issues around digital infrastructure which we have yet to adequately address, and which continue to undermine the path to scale. For example, the data capture mechanisms required to facilitate reimbursement for AI-enabled tools—a vital incentive to procure a specific technology in insurance-based health care systems—are effectively nonexistent in many countries; they are weak at best in many high-income countries as well. AI-solution vendors instead must create bespoke mechanisms for getting paid, and thus, unsurprisingly, the result is a fragmented market of AI tools which struggles to thrive because the costs of scaling are prohibitively high.
We need government officials and policymakers to understand that there are a series of enabling investments that need to be made to support a well-functioning public and private marketplace for AI-based solutions. Without them, the local entrepreneurial ecosystem will fail to thrive, and local health care systems will have access to limited options.