We know that AI could prove highly beneficial for radiologists by cutting down on read times and improving accuracy. In addition, AI could be a strong resource for mining large data sets for both individual patient care and global insights. But first, we must access the images.
Today’s traditional hardware, CDs, and PACS (picture archiving communications system) lock data deep inside them and prevent interoperability.
I’d never considered this before but it’s obviously true, and for good reason. Patient anonymity and privary are good reasons to lock down medical images. But it also means that we won’t be able to run the kinds of machine learning algorithms on that data, nor will we be able to compare data from different populations where the medical images sit on different servers in different countries and are regulated by different laws and policies.
If we want to see the kinds of progress being made in other areas of image classification, we may need to reconsider our current policies around sharing patient data. Of course we’ll need consent from patients, as well as a means of ensuring data transfer across systems. This second point alone would be worth pursuing anyway, as it may lead to a set of (open) standards for interoperability between different EHR systems.
As with all things related to machine learning, having access to high fidelity, well-labelled data is key. If we don’t make patient data accessible in some format or another we may find it hard to use AI-based systems in healthcare. This obviously assumes that we want AI-based systems in healthcare in the first place.