Challenges in digital medicine applications in under-resourced settings

Digital medicine tools, including medical AI, have been advocated as potential game-changers to solve long-standing healthcare access and treatment inequality issues in low and middle income countries. For example, automated tools can fill gaps in trained healthcare workforce availability and even augment equipment capabilities. As these applications are increasingly becoming a reality, we connect here with researchers with experience in planning and deployment of these tools in under-resourced settings, to understand what the current bottlenecks are from a practical point of view. The experts involved are Walter H. Curioso (a digital health expert and biomedical informatics consultant, Universidad Continental, University of Washington), Daniel S.W. Ting (an expert in deep learning for medical imaging and digital innovation, Singapore Health Service, Duke-NUS Medical School), Bram van Ginneken (an expert in machine learning for medical image analysis, Radboud University) and Martin C. Were (an expert in digital health and medical informatics, Vanderbilt University Medical Center).