  A longstanding challenge surrounding deep learning algorithms is unpacking
and understanding how they make their decisions. Explainable Artificial
Intelligence (XAI) offers methods to provide explanations of internal functions
of algorithms and reasons behind their decisions in ways that are interpretable
and understandable to human users. . Numerous XAI approaches have been
developed thus far, and a comparative analysis of these strategies seems
necessary to discern their relevance to clinical prediction models. To this
end, we first implemented two prediction models for short- and long-term
outcomes of traumatic brain injury (TBI) utilizing structured tabular as well
as time-series physiologic data, respectively. Six different interpretation
techniques were used to describe both prediction models at the local and global
levels. We then performed a critical analysis of merits and drawbacks of each
strategy, highlighting the implications for researchers who are interested in
applying these methodologies. The implemented methods were compared to one
another in terms of several XAI characteristics such as understandability,
fidelity, and stability. Our findings show that SHAP is the most stable with
the highest fidelity but falls short of understandability. Anchors, on the
other hand, is the most understandable approach, but it is only applicable to
tabular data and not time series data.
