The Chair will address the topic of AI in two specific scenarios to indeed contribute with real-life case studies to showcase AI as a feasible approach towards meeting initially two and expanding to other SDGs.
1. Sustainable Development Goal 3: Good Health and Well-being
Machine learning is already contributing to improved diagnoses and treatment of diseases. Quicker accurate malaria diagnosis will enable faster delivery of clinical services to facilitate International Development Goals for the sub-Saharan African region and other regions of the World affected with malaria. The funding will be used to carry-out engineering (robotics), computational research (computer vision and machine learning) and digital health clinical research (pediatric infectious diseases) to design, implement, deploy and test a fully automated system capable of tackling the challenges posed by human-operated light-microscopy currently used in the diagnosis of malaria. The funded research aims to overcome these diagnostic challenges by replacing human-expert optical-microscopy with a robotic automated computer-expert system that assesses similar digital-optical-microscopy representations of the disease. The Fast, Accurate and Scalable Malaria (FASt-Mal) diagnosis system harnesses the power of state-of-the-art machine learning approaches to support clinical decision making. Driven by AI in each step, this allows for constant improvement and scalability. Improved smartphone processing also has potential to enable diagnostics “on-the-go” or in remote areas. As smartphone penetration increases, access to mobile diagnostics will expand, magnifying the effect of the improvements in smartphone processing enabling these innovations.
Sustainable Development Goal 4: Quality Education
Developments in machine learning can raise educational standards through improved educational apps, digital engagement, and personalised learning. The X5gon: Cross Modal, Cross Cultural, Cross Lingual, Cross Domain, and Cross Site Global OER Network project leverages AI to deliver personalized learning. This solution will adapt to the user’s needs and learn how to make ongoing customized recommendations and suggestions through a truly interactive and impactful learning experience. This new AI-driven platform will deliver OER content from everywhere, for the students and teachers need at the right time and place. X5GON will develop innovative services for large scale learning content understanding, large scale user modelling and real-time learning personalization with a main processing pipeline dealing with big data analytics in near to real time setting. X5GON analytics pipeline is not only relevant for OER but can be easily applied in other domains as well. The term Open Educational Resources has been introduced by UNESCO in 2002, and adopted OER as a strategy to meet its objectives in education.