  Machine Learning (ML) has recently been demonstrated to rival expert-level
human accuracy in prediction and detection tasks in a variety of domains,
including medicine. Despite these impressive findings, however, a key barrier
to the full realization of ML's potential in medical prognoses is technology
acceptance. Recent efforts to produce explainable AI (XAI) have made progress
in improving the interpretability of some ML models, but these efforts suffer
from limitations intrinsic to their design: they work best at identifying why a
system fails, but do poorly at explaining when and why a model's prediction is
correct. We posit that the acceptability of ML predictions in expert domains is
limited by two key factors: the machine's horizon of prediction that extends
beyond human capability, and the inability for machine predictions to
incorporate human intuition into their models. We propose the use of a novel ML
architecture, Neural Ordinary Differential Equations (NODEs) to enhance human
understanding and encourage acceptability. Our approach prioritizes human
cognitive intuition at the center of the algorithm design, and offers a
distribution of predictions rather than single outputs. We explain how this
approach may significantly improve human-machine collaboration in prediction
tasks in expert domains such as medical prognoses. We propose a model and
demonstrate, by expanding a concrete example from the literature, how our model
advances the vision of future hybrid Human-AI systems.
