In Africa, an infrastructure that utilizes an AI “ecosystem” could help place radiology practices in a central network with other stakeholders and researchers throughout the continent and help close gaps in care for underserved patients.
But what if technology can’t get to those populations or doesn’t work when it gets there?
Factors like infrequent scanner maintenance, network connectivity hiccups and unreliable power sources make it challenging to apply AI in a clinical setting in resource-constrained countries. High staff turnover and overworked personnel, sporadic access to contrast materials and a general lack of clinical imaging protocols create a recipe for ambiguity and uncertainty.
“All of these factors result in lower quality and less breadth of data for developing high-value imaging innovations,” according to Udunna Anazodo, PhD, assistant professor of neurology and neurosurgery at the Montreal Neurological Institute at McGill University, Quebec, and coauthor of the Radiology: Artificial Intelligence editorial, “AI for Population and Global Health in Radiology.” “More importantly, the lack of national data policy and regulatory frameworks make adoption of routine AI imaging solutions by and large unattainable in Africa.”
Collaboration To Create AI Solutions
Dr. Anazodo directs the Africa Neuroimaging Archive (AfNiA), a project seeking to bring together key opinion leaders in imaging data science and experts in African health care delivery to enable the formulation of policy and regulatory frameworks for discovery and implementation of AI imaging innovations in Africa.
AfNiA will be creating a publicly available imaging repository of annotated brain MRI studies aggregated from the region to cultivate homegrown AI innovations as the standard of care. However, according to Dr. Anazodo, each AI tool needs clinical validation and regulatory approval. These processes require collaboration among stakeholders— computational imaging scientists, imaging physicists, radiologists, the industry partners that roll out the tools and the agencies that regulate and fund health care delivery.
In addition, for resource-constrained countries to benefit from global and population health AI solutions, models must be capable of producing imaging biomarkers that enable country-specific and population-level assessments of relevant disease traits and trends. Barriers also have to be broken across all settings, from those where resources are limited to those where they are plentiful, Dr. Anazodo noted. If initiatives like AfNiA can deliver AI imaging innovation solutions to Africa’s complex health care problems, those solutions can be readily adopted in other resource-constrained settings, as compared to solutions from better resourced regions that may not be as easily adaptable.
“More effort should be put into understanding the problem from a population health standpoint and into designing scalable solutions in which AI is a part of a larger intervention, guided by rigorous implementation of science strategies,” said editorial coauthor Farouk Dako, MD, MPH, director of RAD-AID Nigeria, an international organization that brings radiology to resource-constrained areas by delivering education, equipment, infrastructure and support.