Crowd Sourcing and Disease Recognition

I came across BioGames, a product of UCLA's Ozcan Research Group, that uses a web game that players can play that helps doctors find malaria-infected red blood cells. The group has shown that "by utilizing the innate visual recognition and learning capabilities of human crowds it is possible to conduct reliable microscopic analysis of biomedical samples and make diagnostics decisions based on crowd-sourcing of microscopic data through intelligently designed and entertaining games that are interfaced with artificial learning and processing back-ends."

Why it works: "So far we have shown that this platform is capable of achieving high accuracies in diagnosing red blood cells that are potentially infected with malaria. We have shown this on a small scale with up to 30 gamers. We need your help to scale this up into a truly massively crowd-sourced platform. When you play a game, your responses are collected and combined with those of other individuals to produce an accurate overall diagnosis. Our goal is to achieve the same accuracy level of a medical professional. Having shown that the crowd's accuracy increases with the size of the crowd, we are interested in finding the most optimal number of individuals needed for accurate diagnosis."

Even more overwhelming is the fact that "using non-professional gamers we report diagnosis of malaria infected red-blood-cells with an accuracy that is within 1.25% of the diagnostic decisions made by a trained professional." Therefore, it is possible to aid third world nations that don't have the resources or trained professionals to examine microscopic data.

A great example of the power of crowd-sourcing and online collaboration put to good use; the same power that also controls the more negative aspects of human flesh search.