Radiology

Radiology is changing, but not as fast as many thought. But is there a catalyst on the horizon?

After its AlexNet moment, AI was quickly aimed at medical images in radiology, in the form of neural networks. It made sense to everyone outside the field and produced a few famous claims that radiologists would soon become irrelevant. But, every startup that sought to replace radiologists has disappeared or pivoted. It’s been, speaking broadly, a bumpy road. Medicine remains a human endeavour.

The technical aspects of image acquisition are fascinating. It is the product of decades of research and development of very bright engineers, physicists, developers, material scientists, etc. These decades of effort have optimised image quality for interpretation by a medically-trained human. This point is worth repeating. Years of effort have optimised image quality in a largely qualitative way for various clinical scenarios. These improvements have been aimed to improve the suitability of the images for interpretation by a human - often on the balance of compromises such as radiation or scan time reductions.

Are the most useful images for a human also the most useful images for a classification or other inference algorithm? That seems unlikely. Then, when will we optimise the image acquisition, perhaps the engineering of the scanners themselves, to the benefit of our algorithms instead of humans?

It appears steps are already being taken in this direction.