Inclusive Artificial Intelligence for Accessible Medical Imaging Across Resource-Limited Settings
AIMIX, Inclusive Artificial Intelligence for Accessible Medical Imaging Across Resource-Limited Settings, will develop the first scientific framework for inclusive AI in medical imaging, and demonstrate its relevance for accessible and effective obstetric ultrasound screening in resource-limited rural settings. The project will greatly advance the state-of-the-art in AI for medical imaging, from the existing methods developed in high-income societies and mostly focused on performance as well as trustworthiness, towards new inclusive AI approaches that take into close consideration the local contextual factors and unmet clinical needs in resource-limited settings. To this end, a range of novel integrative-adaptive learning methods will be investigated to intelligently integrate existing large-scale, high-quality imaging cohorts with smaller, low-cost imaging datasets from resource-limited settings. AIMIX will enable the future development of imaging AI algorithms that are fundamentally inclusive, i.e:
- Affordable for resource-limited healthcare centres;
- Scalable to under-represented population groups;
- Accessible to minimally trained clinical workers.
Importantly, AIMIX will investigate the socio-ethical principles and requirements that govern inclusive AI, and examine how they compare, conflict or complement those of trustworthy AI developed thus far in high-income settings.
University of Barcelona
Centre of Excellence in Women and Child Health, Aga Khan University
University of Copenhagen
Karim Lekadir (BCN-AIM)
UB's Principal Investigator