Accelerating AI for global health through crowdsourcing

Poster by Geoffrey Henry Siwo: The promise of artificial intelligence (AI) in medicine is advancing rapidly driven by exponential growth in computing speed, data and new modeling techniques such as deep learning. Unfortunately, advancements in AI stand to disproportionately benefit diseases that predominantly affect the developed world because the key ingredients for AI – computational resources, big data and AI expertise – are less accessible in the developing world. Our research on automated mining of biomedical literature indicates that adoption of machine learning algorithms in global health, for example to understand malaria, lags several years behind diseases like cancer.

 To shift these inequities, we have been exploring the use of crowdsourced data science challenges as a means to rapidly advance computational models in global health. Data science challenges involve seeking computational solutions for specific, well-defined questions from anyone in the world. Here we describe key lessons from our work in this area and the potential value of data science challenges in accelerating AI for global health.

In one of our first initiatives in this area – the Malaria DREAM Challenge – we invited data scientists from across the world to develop computational models that predict the in vitro and in vivo drug sensitivity of malaria parasites to artemisinin using gene expression datasets. More than 360 individuals drawn from academia, government and startups across 31 countries participated in the challenge. Approximately 100 computational solutions to the problem were generated within a period of 3 months. In addition to this sheer volume of participation, a diverse range of modeling approaches including artificial neural networks and automated machine learning were employed….(More)”.