Improving Trustworthiness of AI Disease Severity Rating in Medical Imaging with Ordinal Conformal Prediction Sets. (arXiv:2207.02238v1 [cs.LG])

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 …

ai arxiv disease imaging lg medical medical imaging ordinal prediction