Legal aspects of data cleansing in medical AI
Karl Stöger, David Schneeberger, Peter Kieseberg, Andreas Holzinger
Abstract
Data quality is of paramount importance for the smooth functioning of modern data-driven AI applications with machine learning as a core technology. This is also true for medical AI , where malfunctions due to "dirty data" can have particularly dramatic harmful implications. Consequently, data cleansing is an important part in improving the usability of (Big) Data for medical AI systems. However, it should not be overlooked that data cleansing can also have negative effects on data quality if not performed carefully. This paper takes an interdisciplinary look at some of the technical and legal challenges of data cleansing against the background of European medical device law, with the key message that technical and legal aspects must always be considered together in such a sensitive context.