Most healthcare organizations are puzzling through the challenges and opportunities presented by emerging AI, but that puzzle is significantly more difficult to piece together in parts of the world that don’t yet have the established medical infrastructure of, say, Europe and the US.
With those still-developing regions very much in mind, then, a group of humanitarian organizations has taken advantage of the spotlight provided by the current UN General Assembly to unveil a new initiative that aims at getting developing countries onboard, among other things, the AI bandwagon.
Dubbed “The Precision Public Health initiative” and led by the Rockefeller Foundation, UNICEF, and the World Health Organization, among others, the consortium has pledged an initial $100 million “to empower community health systems and frontline health workers with the latest data science innovations, including more accurate and precise decision-making tools based on large, integrated datasets, predictive analytics, artificial intelligence, and machine learning.”
The initiative will launch in India and Uganda, where backers hope to introduce tech-powered tools to prevent the deaths of mothers and children. Overall, the initiative aims to prevent 6 million deaths in 10 countries by 2030, although the eight additional countries have yet to be announced.
“We have an unprecedented opportunity to leverage advances in data science and technology that have enriched the lives of society’s most privileged, and transform health for those left behind around the world,” said Dr. Rajiv J. Shah, President of The Rockefeller Foundation at the time of the announcement. “Working together, we can close the health inequity gap by driving innovation and investment to save millions of lives.”
According to David Mitchell, managing director of innovation for health at the Rockefeller Foundation, “the precision tools will be built using existing health datasets collected by health workers on the front lines or by governments and then augmented with datasets not traditionally associated with health.”
Mitchell went on to explain that, at least initially, the initiative will not try to “translate a lot of paper-based information that’s still dominant in a lot of the geographies that we’re looking at.”
Rather, Mitchell said, “we are trying to focus on data that already has a quality level that is reassuring, and then we’re going to further validate that the data entries make sense, that there aren’t any anomalies, that there aren’t a lot of blanks, that there aren’t a lot of strange, repetitive metrics in these fields.”