  With the increasing availability of AI-based decision support, there is an
increasing need for their certification by both AI manufacturers and notified
bodies, as well as the pragmatic (real-world) validation of these systems.
Therefore, there is the need for meaningful and informative ways to assess the
performance of AI systems in clinical practice. Common metrics (like accuracy
scores and areas under the ROC curve) have known problems and they do not take
into account important information about the preferences of clinicians and the
needs of their specialist practice, like the likelihood and impact of errors
and the complexity of cases. In this paper, we present a new accuracy measure,
the H-accuracy (Ha), which we claim is more informative in the medical domain
(and others of similar needs) for the elements it encompasses. We also provide
proof that the H-accuracy is a generalization of the balanced accuracy and
establish a relation between the H-accuracy and the Net Benefit. Finally, we
illustrate an experimentation in two user studies to show the descriptive power
of the Ha score and how complementary and differently informative measures can
be derived from its formulation (a Python script to compute Ha is also made
available).
