Is data drift an issue for medical AI models - and what can we do about it?

While medical AI has many potential benefits, there are challenges that have not been tackled yet. One of the major challenges is the limited generalizability of many AI algorithms. Applying a medical AI algorithm that is trained in hospital A may give unexpected results when applied in hospital B. Why does this happen? Certain parameters are different between hospitals. If hospital A has Siemens CT scanners and hospital B has Philips CT scanners, the images look different. If the AI algorithm is only trained on Siemens CT images, it will probably perform less well on Philips CT images. This is only one example of a parameter that may differ, there are many others such as age and race distribution of the patient cohort, imaging acquisition and reconstruction protocols, etc. The solution for this problem would be to train the AI algorithm with a large heterogeneous dataset.