  There is significant interest in deploying machine learning algorithms for
diagnostic radiology, as modern learning techniques have made it possible to
detect abnormalities in medical images within minutes. While machine-assisted
diagnoses cannot yet reliably replace human reviews of images by a radiologist,
they could inform prioritization rules for determining the order by which to
review patient cases so that patients with time-sensitive conditions could
benefit from early intervention.
  We study this scenario by formulating it as a learning-augmented online
scheduling problem. We are given information about each arriving patient's
urgency level in advance, but these predictions are inevitably error-prone. In
this formulation, we face the challenges of decision making under imperfect
information, and of responding dynamically to prediction error as we observe
better data in real-time. We propose a simple online policy and show that this
policy is in fact the best possible in certain stylized settings. We also
demonstrate that our policy achieves the two desiderata of online algorithms
with predictions: consistency (performance improvement with prediction
accuracy) and robustness (protection against the worst case). We complement our
theoretical findings with empirical evaluations of the policy under settings
that more accurately reflect clinical scenarios in the real world.
