Machine learning can solve some of Africa’s biggest challenges
From better access to healthcare to improved food security, machine learning could address a wide range of challenges in developing countries.
In 2020, a study published in Nature showed that Google’s machine-learning artificial intelligence program, 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 74% for radiologists. Imagine what a difference deploying 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 beginning.
Marilyn Moodley, Country Manager, South Africa and West, East and Central Africa (WECA), SoftwareONE, said machine learning can help solve some of the region’s most pervasive problems, from reducing poverty and improving education to providing health care and addressing sustainability issues such as food demand. “Machine learning is democratizing access to innovative and productivity-boosting technologies to fuel the growth the continent needs. It fundamentally reshapes the way work is done, enabling more efficient allocation of resources leading to increased productivity and, in the case of government, improving service delivery to citizens,” Moodley said.
According to Moodley, the agricultural sector employs over 65% of Africa’s workforce and accounts for 32% of gross domestic product (GDP). “The World Bank estimates that African food markets will reach $1 trillion by 2030, up from $300 billion today. Food demand is expected to at least double by 2050, but the sector is burdened with limitations. Land is degrading, soils are becoming less fertile, water tables are falling, pests are becoming more resistant and the climate is more vulnerable and unpredictable. All of 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 stages of the agricultural value chain, she says. “These technologies can empower smallholder farmers to increase their income through improved crop yields and better price control. For example, crop data analysis 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 negotiate better prices with suppliers.
Machine learning can not only analyze tests and images to suggest diagnoses, but also aggregate data and update patient records. It is also rapidly expanding into other areas of health, including early disease detection, treatment and research, Moodley noted. “It would free up the workload for doctors, allowing them to spend more time with patients and on actual patient care. Japan is already planning to augment its doctors with artificial intelligence to combat doctor shortages,” she said.
Moodley pointed out that in Africa, machine learning could fill the same gap, but also address other challenges. “Health systems in Africa face several structural challenges such as shortage of qualified professionals or supplies, leading to divergent outcomes for patients. Even when facilities and staff are available, affordability and rural-urban disparities can put needed services out of reach for patients,” she said.
According to Moodley, machine learning can improve these results in the following ways:
Improving health care delivery: Advanced data analytics can help practitioners quickly identify potential issues and better tailor preventive care. Early interventions make health care more affordable and easier for the patient, with better outcomes.
Better diagnosis and detection: Analyzing patterns in data, such as X-ray machine vision analysis, can make diagnoses faster and more accurate.
Improved access: Tools such as chatbots can extend access to millions of people and remotely diagnose various health conditions using images from everyday smartphone cameras.
Moodley pointed out that: “Machine learning is a powerful tool that can benefit multiple industries, including marketing, financial services, transportation and manufacturing. The possible use cases are limitless and clearly demonstrate the importance of innovative technology to ensure efficient business processes. »
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