Low and Middle-Income Settings

This theme includes a number of initiatives that seek to improve access to healthcare in low- and middle-income countries (LMICs). Using AI-based algorithms within smartphones and wearables, we are able to use inexpensive sensors that are appropriate for use at scale in LMICs.  The delivery of healthcare in such settings is often performed by healthcare workers without high levels of clinical training, and so there is therefore a need for the decision-support capabilities of such algorithms.



This work builds on the Wellcome Trust’s first “Flagship Innovation Centre”, which joins CHI Lab in Oxford to the Oxford University Clinical Research Unit in Vietnam; the goal of this major programme is to develop innovations for improving access to healthcare in south-east Asia and beyond.  We also work closely with experts in overseas development at the School of Geography & the Environment at Oxford to deliver technology-based systems for improving water security in LMICs.

The BSP-ML group has a joint project with the Centre for Tropical Medicine & Global Health in Kilifi, Kenya, to identify childhood pneumonia from the analysis of video data recorded with smartphone cameras, using a combination of computer vision and machine learning algorithms.