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As many as 𝟰 𝗼𝗳 𝟱 of the world's patient data sets come from people of European descent. How can we develop therapies for all populations if our algorithms are trained on data from mostly one group? 📈 “Relying on data that represents too narrow a population will be full of gaps and biases, which the AI and machine learning algorithms will learn,” says Genialis’ CEO Rafael Rosengarten. In a recent Forbes article, Rafael discussed how Genialis is working to change this by assembling the world's most 𝗲𝘁𝗵𝗻𝗼-𝗴𝗲𝗼𝗴𝗿𝗮𝗽𝗵𝗶𝗰𝗮𝗹𝗹𝘆 𝗱𝗶𝘃𝗲𝗿𝘀𝗲 𝗰𝗮𝗻𝗰𝗲𝗿 𝗱𝗮𝘁𝗮 𝘀𝗲𝘁𝘀. 🌍 Check the link in the comments to read the full article by Jennifer Kite-Powell. 📷: Mario Tama/Getty Images

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