Machine Learning and AI

Healthcare

ML and artificial intelligence (AI) techniques are applied in many domains of healthcare. In disease diagnosis, computer vision is used for detecting cancerous tumors using computed tomography (CT) scans. Similarly, AI and ML techniques have been used to help molecules and drug discovery. Effective use of electronic healthcare records (EHRs) can reduce the time clinicians spend on managing patients' data, accelerate diagnosis, improve patients' triage and management, enable continuous care across time and geography, and much more.

Outbreaks preparedness, management, and prevention are critical to the healthcare strategies throughout the African continent. ML and AI techniques can help predict outbreaks and their evolutions.

Telemedicine is one of the technologies that can revolutionize healthcare delivery in rural Africa. Patients in rural areas can receive high quality care through telehealth. Specialist practitioners from large cities can dedicate some of their time to rural areas and help improve health assessments. Family members from cities can remotely help in the interactions with the healthcare system. The pharmacies can remotely serve rural areas.

Agriculture

Applications of AI and ML techniques in Agriculture are numerous and some of them can be critical to Africa improving its production. Applications of AI and ML in agriculture include

• Improving crop yield prediction by using real-time data and computer vision techniques.
• Pest management using in-ground sensors and optimized systems to deliver pesticides or biological pest control.
• Tracking systems for better efficiency of the agricultural supply chains.
• Price forcasting for crops based on yields rates.
• Finding irrigation leaks, optimizing irrigation systems, measuring irrigation effectiveness, etc.

AI and ML techniques can also be used for monitoring livestock's health and growth. Vaccine and medicine intakes can be automated. Local markets can be mapped and demand monitored to optimize the production and delivery of livestock products.

Population and Geo-Referenced Data

The African population is growing fast and decennial censuses are not able to provide local and real-time population counts and densities. A lot of work has been done during the last decade to produce high-resolution population data. These maps can be used to help tackle many issues around the continent, e.g. optimizing health service delivery by knowing population counts, tracking populations movements during disasters and conflict, planning public services and commercial activities, and much more.

Local and Real-Time Data

Decision-making will tremendously benefit by using insights derived from current and granular data. Given the geo-referenced data, social media, high-speed internet, large computing power, and proven AI and ML algorithm, QuantifyAfrica believes that more can be done in producing local and real-time information.