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 1,000 people, according to the World Bank. And that’s just the start.
Marilyn Moodley, Country Leader, South Africa and West, East, Central Africa (WECA), SoftwareONE, said 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. “Machine Learning democratises access to innovative and productivity-boosting technology to fuel the growth the continent needs. It’s fundamentally reshaping how work is done, allowing for a more efficient allocation of resources leading to increased productivity and, in the case of government, improving the delivery of services to citizens,” said Moodley.
Agricultural improvements
According to Moodley, the agriculture sector employs over 65% of Africa’s labour force and accounts for 32% of gross domestic product (GDP). “The World Bank estimates that African food markets will be worth US$1 trillion by 2030, up from the current US$300 billion. Demand for food is projected to at least double by 2050, yet the sector is burdened with limitations. Land is degrading, soil is becoming less fertile, water tables are dropping, pests are becoming more resistant, and the climate is more vulnerable and unpredictable. All this could have disastrous effects on food availability in the future,” she said.
Machine learning has the potential to improve productivity and efficiency at all the stages of the agricultural value chain, she says. “These technologies can empower small-holder farmers to increase their income through higher crop yield and greater price control. For example, analytics of crop data can help identify diseases, enable soil health monitoring without the need for laboratory testing infrastructure, and facilitate the creation of virtual cooperatives to aggregate crop yields and broker better prices with suppliers.”
Healthcare developments
Machine Learning cannot only analyse tests and images to suggest diagnoses, but also aggregate data and update patients’ charts. It’s also rapidly expanding into other healthcare areas, including early detection of diseases, treatment and research, noted Moodley. “This would free up physicians’ workloads, allowing them to spend more time with patients and on actual patient care. Japan is already looking at augmenting its doctors with Artificial Intelligence to combat doctor shortfalls,” she said.
Moodley pointed out that in Africa, Machine Learning could plug the same gap, but also address other challenges. “Health systems in Africa face several structural challenges such as shortages of qualified professionals or supplies, resulting in divergent outcomes for patients. Even when facilities and staff are available, affordability and rural/urban disparities can put needed services out of reach of patients,” she said.
According to Moodley, Machine Learning can enhance these outcomes in the following ways:
Improve healthcare delivery: Advanced data analytics can help practitioners identify potential problems early and tailor better preventive care. Early interventions make healthcare more affordable and easier for the patient, with better outcomes.
Better diagnostics and detection: Analysing patterns in data, such as machine vision analysis of x-rays, can make diagnoses faster and more accurate.
Improved access: Tools such as online conversation agents can extend access to millions of people and remotely diagnose various health conditions using images from the cameras of everyday smartphones.
Moodley pointed out that: “Machine Learning is a powerful tool that can benefit multiple industries, including marketing, financial services, transportation and manufacturing. Possible use cases are boundless and clearly demonstrate the importance of innovative technology for ensuring efficient business processes.”
