  The recent spike in certified Artificial Intelligence (AI) tools for
healthcare has renewed the debate around adoption of this technology. One
thread of such debate concerns Explainable AI (XAI) and its promise to render
AI devices more transparent and trustworthy. A few voices active in the medical
AI space have expressed concerns on the reliability of Explainable AI
techniques and especially feature attribution methods, questioning their use
and inclusion in guidelines and standards. Despite valid concerns, we argue
that existing criticism on the viability of post-hoc local explainability
methods throws away the baby with the bathwater by generalizing a problem that
is specific to image data. We begin by characterizing the problem as a lack of
semantic match between explanations and human understanding. To understand when
feature importance can be used reliably, we introduce a distinction between
feature importance of low- and high-level features. We argue that for data
types where low-level features come endowed with a clear semantics, such as
tabular data like Electronic Health Records (EHRs), semantic match can be
obtained, and thus feature attribution methods can still be employed in a
meaningful and useful way. Finally, we sketch a procedure to test whether
semantic match has been achieved.
