  Alzheimer's disease (AD) has a complex and multifactorial etiology, which
requires integrating information about neuroanatomy, genetics, and
cerebrospinal fluid biomarkers for accurate diagnosis. Hence, recent deep
learning approaches combined image and tabular information to improve
diagnostic performance. However, the black-box nature of such neural networks
is still a barrier for clinical applications, in which understanding the
decision of a heterogeneous model is integral. We propose PANIC, a prototypical
additive neural network for interpretable AD classification that integrates 3D
image and tabular data. It is interpretable by design and, thus, avoids the
need for post-hoc explanations that try to approximate the decision of a
network. Our results demonstrate that PANIC achieves state-of-the-art
performance in AD classification, while directly providing local and global
explanations. Finally, we show that PANIC extracts biologically meaningful
signatures of AD, and satisfies a set of desirable desiderata for trustworthy
machine learning. Our implementation is available at
https://github.com/ai-med/PANIC .
