Open AccessUncertainty-aware deep learning in healthcare: A scoping review
Author(s) -
Tyler J. Loftus,
Benjamin Shickel,
Matthew M. Ruppert,
Jeremy A. Balch,
Tezcan OzrazgatBaslanti,
Patrick Tighe,
Philip A. Efron,
William R. Hogan,
Parisa Rashidi,
Gilbert R. Upchurch,
Azra Bïhorac
Publication year - 2022
Publication title -
plos digital health
Language(s) - English
Resource type - Journals
ISSN - 2767-3170
DOI - 10.1371/journal.pdig.0000085
Subject(s) - computer science , artificial intelligence , machine learning , deep learning , uncertainty quantification , convolutional neural network , health care , dropout (neural networks) , economics , economic growth
Mistrust is a major barrier to implementing deep learning in healthcare settings. Entrustment could be earned by conveying model certainty, or the probability that a given model output is accurate, but the use of uncertainty estimation for deep learning entrustment is largely unexplored, and there is no consensus regarding optimal methods for quantifying uncertainty. Our purpose is to critically evaluate methods for quantifying uncertainty in deep learning for healthcare applications and propose a conceptual framework for specifying certainty of deep learning predictions. We searched Embase, MEDLINE, and PubMed databases for articles relevant to study objectives, complying with PRISMA guidelines, rated study quality using validated tools, and extracted data according to modified CHARMS criteria. Among 30 included studies, 24 described medical imaging applications. All imaging model architectures used convolutional neural networks or a variation thereof. The predominant method for quantifying uncertainty was Monte Carlo dropout, producing predictions from multiple networks for which different neurons have dropped out and measuring variance across the distribution of resulting predictions. Conformal prediction offered similar strong performance in estimating uncertainty, along with ease of interpretation and application not only to deep learning but also to other machine learning approaches. Among the six articles describing non-imaging applications, model architectures and uncertainty estimation methods were heterogeneous, but predictive performance was generally strong, and uncertainty estimation was effective in comparing modeling methods. Overall, the use of model learning curves to quantify epistemic uncertainty (attributable to model parameters) was sparse. Heterogeneity in reporting methods precluded the performance of a meta-analysis. Uncertainty estimation methods have the potential to identify rare but important misclassifications made by deep learning models and compare modeling methods, which could build patient and clinician trust in deep learning applications in healthcare. Efficient maturation of this field will require standardized guidelines for reporting performance and uncertainty metrics.