Inclusive Artificial Intelligence for Accessible Medical Imaging Across Resource-Limited Settings
AIMIX will develop the first scientific framework for inclusive imaging AI in resource-limited settings
Project validated in rural Africa, specifically in Kenya
In the age of Big Data and digital health, great excitement has been generated around the extraordinary opportunities that artificial intelligence (AI) may offer in tomorrow’s healthcare. In particular, deep learning-based methods have shown great promise for the analysis of complex biomedical data. In medical imaging, deep learning has already made an impact in a wide range of applications such as cardiology, cancer or brain imaging among many others. However, existing developments have been mostly focused on applications in high-resource settings, where there is greater access to large clinical imaging datasets for training deep learning networks. Applications in environments with limited resources, such as countries across the African continent, have been scarce. Thus, there is a risk of increasing inequalities in global health due to the disparities in the application of AI in medical imaging.
I am delighted to report that my ERC Consolidator Grant on Inclusive Artificial Intelligence for Accessible Medical Imaging Across Resource-Limited Settings (AIMIX) started on 01 January 2023. For the kick off meeting, my team and I met amazing and inspiring healthcare workers and researchers in Kenya. We also visited local clinical centres in rural Kenya, which will be instrumental to implement innovative, inclusive, and accessible AI tools in low-resource settings.
Karim Lekadir, Director of BCN-AIM, Ramon y Cajal researcher at the Faculty of Mathematics and Computer Science of the UB, received 2.2 million euros to conduct the research study Inclusive Artificial Intelligence for Accessible Medical Imaging Across Resource-Limited Settings (AIMIX). “The aim of the project is to study how artificial intelligence can adjust to the needs and features of local populations with limited resources and high healthcare demands, such as those in Africa”