  AI-based digital twins are at the leading edge of the Industry 4.0
revolution, which are technologically empowered by the Internet of Things and
real-time data analysis. Information collected from industrial assets is
produced in a continuous fashion, yielding data streams that must be processed
under stringent timing constraints. Such data streams are usually subject to
non-stationary phenomena, causing that the data distribution of the streams may
change, and thus the knowledge captured by models used for data analysis may
become obsolete (leading to the so-called concept drift effect). The early
detection of the change (drift) is crucial for updating the model's knowledge,
which is challenging especially in scenarios where the ground truth associated
to the stream data is not readily available. Among many other techniques, the
estimation of the model's confidence has been timidly suggested in a few
studies as a criterion for detecting drifts in unsupervised settings. The goal
of this manuscript is to confirm and expose solidly the connection between the
model's confidence in its output and the presence of a concept drift,
showcasing it experimentally and advocating for a major consideration of
uncertainty estimation in comparative studies to be reported in the future.
