When pharma skips diverse populations, the result isn’t just bias—it’s failure. Overlooked groups mean misdiagnoses, ineffective treatments, and failed drug launches, costing pharma billions. Can we afford to exclude 84% of the world?
Drug response varies by genetics and lifestyle. That’s why diverse data isn’t a nice-to-have—it’s a scientific necessity. Without it, we miss key biomarkers, leaving billions of people behind in the race toward precision medicine.
Caucasians make up just 16% of the global population—yet they represent 82% of AI training data in healthcare. That leaves most of the world invisible to the very tools meant to help us. The future of medicine can’t be built on exclusion.
AI is only as good as the data it’s fed. If that data isn’t inclusive, outcomes won’t be either. Bias in means bias out — and in healthcare, that leads to misdiagnoses, mistreatment, and lost lives. Diverse data isn’t optional—it’s essential.
What if cancer AI worked for everyone, not just a select few? Most cancer AI is built on data from just 16% of the global population, primarily from homogeneous, Western datasets. At PAICON, we’re closing the remaining 84% gap.
Our mission is to bring equity to cancer diagnostics by training AI on diverse, representative data from across the world.
Whether you're exploring collaboration or simply curious about our work, our video will give you a quick understanding of our mission. Take a minute to see how we're challenging the status quo and contact us to explore how you can be part of the change.