The use of Artificial Intelligence (AI) systems shows promise in medicine, where
they can be used to detect diseases earlier, improve treatments, and ease staff
workloads. But their performance depends on how well the AI is trained. A new
multi-task approach to training AI makes it possible to train foundation models
quicker and more cost-effectively, with less data. Researchers are turning to this
approach to compensate for the shortage of data in medical imaging — and ultimately
save lives.