Artificial intelligence (AI) and data science have the potential to revolutionize global health. But what exactly is AI and what hurdles stand in the way of more widespread integration of big data in global health? Duke’s Global Health Institute (DGHI) hosted a Think Global webinar Wednesday, February 17th to dive into these questions and more.
The webinar’s panelists were Andy Tatem (Ph.D), Joao Vissoci (Ph.D.), and Eric Laber (Ph.D.), moderated by DGHI’s Director of Research Design and Analysis Core, Liz Turner (Ph.D.). Tatem is a professor of spatial demography and epidemiology at the University of South Hampton and director of WorldPop. Vissoci is an assistant professor of surgery and global health at Duke University. Laber is a professor of statistical science and bioinformatics at Duke.
Tatem, Vissoci, and Laber all use data science to address issues in the global health realm. Tatem’s work largely utilizes geospatial data sets to help inform global health decisions like vaccine distribution within a certain geographic area. Vissoci, who works with the GEMINI Lab at Duke (Global Emergency Medicine Innovation and Implementation Research), tries to leverage secondary data from health systems in order to understand issues of access to and distribution of care, as well as care delivery. Laber is interested in improving decision-making processes in healthcare spaces, attempting to help health professionals synthesize very complex data via AI.
All of their work is vital to modern biomedicine and healthcare, but, Turner said, “AI means a lot of different things to a lot of different people.” Laber defined AI in healthcare simply as using data to make healthcare better. “From a data science perspective,” Vissoci said, “[it is] synthesizing data … an automated way to give us back information.” This returned info is digestible trends and understandings derived from very big, very complex data sets. Tatem stated that AI has already “revolutionized what we can do” and said it is “powerful if it is directed in the right way.”
We often get sucked into a science-fiction version of AI, Laber said, but in actuality it is not some dystopian future but a set of tools that maximizes what can be derived from data.
However, as Tatem stated, “[AI] is not a magic, press a button” scenario where you get automatic results. A huge part of work for researchers like Tatem, Vissoci, and Laber is the “harmonization” of working with data producers, understanding data quality, integrating data sets, cleaning data, and other “back-end” processes.
This comes with many caveats.
“Bias is a huge problem,” said Laber. Vissoci reinforced this, stating that the models built from AI and data science are going to represent what data sources they are able to access – bias included. “We need better work in getting better data,” Vissoci said.
Further, there must be more up-front listening to and communication with “end-users from the very start” of projects, Tatem outlined. By taking a step back and listening, tools created through AI and data science may be better met with actual uptake and less skepticism or distrust. Vissoci said that “direct engagement with the people on the ground” transforms data into meaningful information.
Better structures for meandering privacy issues must also be developed. “A major overhaul is still needed,” said Laber. This includes things like better consent processes for patients’ to understand how their data is being used, although Tatem said this becomes “very complex” when integrating data.
Nonetheless the future looks promising and each panelist feels confident that the benefits will outweigh the difficulties that are yet to come in introducing big data to global health. One cool example Vissoci gave of an ongoing project deals with the influence of environmental change through deforestation in the Brazilian Amazon on the impacts of Indigenous populations. Through work with “heavy multidimensional data,” Vissoci and his team also have been able to optimize scarcely distributed Covid vaccine resource “to use in areas where they can have the most impact.”
Laber envisions a world with reduced or even no clinical trials if “randomization and experimentation” are integrated directly into healthcare systems. Tatem noted how he has seen extreme growth in the field in just the last 10 to 15 years, which seems only to be accelerating.
A lot of this work has to do with making better decisions about allocating resources, as Turner stated in the beginning of the panel. In an age of reassessment about equity and access, AI and data science could serve to bring both to the field of global health.
Post by Cydney Livingston