In the age of Big Data and digital health, great excitement has been generated around the extraordinary opportunities that artificial intelligence (AI) may offer in tomorrow’s healthcare. In particular, deep learning-based methods have shown great promise for the analysis of complex biomedical data. In medical imaging, deep learning has already made an impact in a wide range of applications such as cardiology, cancer or brain imaging among many others. However, existing developments have been mostly focused on applications in high-resource settings, where there is greater access to large clinical imaging datasets for training deep learning networks. Applications in environments with limited resources, such as countries across the African continent, have been scarce. Thus, there is a risk of increasing inequalities in global health due to the disparities in the application of AI in medical imaging.