This paper discusses and explores the potential and relevance of recent developments in artificial intelligence (AI) and digital twins for health and well-being in low-resource African countries. We use the case of public health emergency response to disease outbreaks and epidemic control. There is potential to take advantage of the increasing availability of data and digitization to develop advanced AI methods for analysis and prediction. Using an AI systems perspective, we review emerging trends in AI systems and digital twins and propose an initial augmented AI system architecture to illustrate how an AI system can work with a 3D digital twin to address public health goals. We highlight scientific knowledge discovery, continual learning, pragmatic interoperability, and interactive explanation and decision-making as essential research challenges for AI systems and digital twins.

From better healthcare access to improved food security, machine learning could tackle a wide range of challenges in developing countries. In 2020, a study published in Nature showed that Google's machine learning artificial intelligence programme, DeepMind AI, outperformed radiologists in detecting breast cancer. After being trained on thousands of mammograms, the system was able to accurately identify 89% of breast cancer cases, compared to radiologists' 74%. Just imagine what a difference the deployment of such a system could make in sub-Saharan Africa, where there are 0.2 doctors per 1000 people, according to the World Bank. Marilyn Moodley, Country Leader for South Africa and WECA (West, East, Central Africa) at SoftwareONE, says machine learning can help with some of the region's most pervasive problems, from reducing poverty and improving education to delivering healthcare and addressing sustainability challenges such as food demand.

Clustering is frequently used in the energy domain to identify dominant electricity consumption patterns of households, which can be used to construct customer archetypes for long term energy planning. Selecting a useful set of clusters however requires extensive experimentation and domain knowledge. While internal clustering validation measures are well established in the electricity domain, limited research is available for external measures. We present a method that distills expert knowledge into competency questions, which we operationalised as external evaluation measures to specify the clustering objective for our application. This approach supported a structured and formal cluster validation process that combined internal and external measures to select a cluster set that is useful for creating residential electricity customer archetypes from electricity meter data in South Africa. We validated the approach in a case study application where we successfully reconstructed customer archetypes previously developed by experts. Our approach enables transparent and repeatable cluster ranking and selection by data scientists, even if they have limited domain knowledge.

"AI should be used for improving human decision-making and not to replace it," says Moodley. "The danger lies in how we approach the technology. If we use it responsibly, almost as an early warning and recommendation system, it can add immense value. If, however, we rely on it fully to manage our health, then there are dangers. According to Rimmer, another factor is the scale of fallout that could occur. "If there is an error in the system, assuming it is a pervasive system, then everyone could get given the wrong diagnosis/information. One bad doctor can impact only a few people; one bad machine could impact thousands.