  The regulatory approval and broad clinical deployment of medical AI have been
hampered by the perception that deep learning models fail in unpredictable and
possibly catastrophic ways. A lack of statistically rigorous uncertainty
quantification is a significant factor undermining trust in AI results. Recent
developments in distribution-free uncertainty quantification present practical
solutions for these issues by providing reliability guarantees for black-box
models on arbitrary data distributions as formally valid finite-sample
prediction intervals. Our work applies these new uncertainty quantification
methods -- specifically conformal prediction -- to a deep-learning model for
grading the severity of spinal stenosis in lumbar spine MRI. We demonstrate a
technique for forming ordinal prediction sets that are guaranteed to contain
the correct stenosis severity within a user-defined probability (confidence
interval). On a dataset of 409 MRI exams processed by the deep-learning model,
the conformal method provides tight coverage with small prediction set sizes.
Furthermore, we explore the potential clinical applicability of flagging cases
with high uncertainty predictions (large prediction sets) by quantifying an
increase in the prevalence of significant imaging abnormalities (e.g. motion
artifacts, metallic artifacts, and tumors) that could degrade confidence in
predictive performance when compared to a random sample of cases.
