  Advances in computing power, deep learning architectures, and expert labelled
datasets have spurred the development of medical imaging artificial
intelligence systems that rival clinical experts in a variety of scenarios. The
National Institutes of Health in 2018 identified key focus areas for the future
of artificial intelligence in medical imaging, creating a foundational roadmap
for research in image acquisition, algorithms, data standardization, and
translatable clinical decision support systems. Among the key issues raised in
the report: data availability, need for novel computing architectures and
explainable AI algorithms, are still relevant despite the tremendous progress
made over the past few years alone. Furthermore, translational goals of data
sharing, validation of performance for regulatory approval, generalizability
and mitigation of unintended bias must be accounted for early in the
development process. In this perspective paper we explore challenges unique to
high dimensional clinical imaging data, in addition to highlighting some of the
technical and ethical considerations in developing high-dimensional,
multi-modality, machine learning systems for clinical decision support.
