The Facebook/NYU partnership is working to minimize the amount of data that is captured, instead relying on computers to reconstruct the image from imperfect inputs. If this is successful, we may see a 10x reduction in scan times, which would lead to lower costs for MRIs and a much greater utilization of these machines
Machine learning is successful partly because of the ability to make accurate inferences with respect to missing data. In other words, machine learning algorithms predict outcomes based on patterns observed in the data, even when important information is missing. The article describes a project that sees ML algorithms fill in the missing data that occurs when an MRI scan is done too quickly. Currently, scans take a long time because we can’t see the image properly when the data is missing. But if an algorithm can infer what that missing data is with a high level of accuracy, then we can afford to do the scans more quickly and fill in the data algorithmically.
I wonder if there’s a risk of missing something that would have turned up with current scans. For example, a tumour that doesn’t show up in the AI-moderated scan because the algorithm didn’t infer it’s presence.