Accelerating AI for global health through crowdsourcing
Creators: name: Siwo, Geoffrey Henry
name: Malaria DREAM Challenge Consortium
name: Sibey, Carol
name: Ferdig, Michael T
name: Malaria DREAM Challenge Consortium
Publisher: name: Gates Open Research
Year: 2020

description: 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.<br> 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.<br>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 <i>in vitro</i> and <i>in vivo</i> 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.<br> The top models with the highest performance in predicting the <i>in vitro</i> and <i>in vivo</i> artemisinin sensitivity of malaria parasites demonstrated that gene expression measurements may be useful for predicting emerging drug resistance. Importantly, integrating information from models across the Malaria DREAM challenge teams provides more biological insights about the potential role of gene expression in artemisinin response compared to using information from any single team only.<br>Our results show that in addition to accelerating the development of computational models, crowdsourced data science challenges also provide a means to benchmark computational solutions through rigorous and independent evaluation of several models using the same datasets and metrics. Furthermore, crowdsourcing provides a means to harness the wisdom of the crowds by learning from diverse modeling techniques. This ensures that the resulting models are of high quality, transparent, verifiable, reproducible and auditable. Thus, crowdsourced data science challenges could play an important role in accelerating the equitable development of AI-based solutions to global health problems.