Second opinion needed: communicating uncertainty in medical machine learning

Published in

npj Digital Medicine(2021)

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TL;DR

An overview of different approaches to uncertainty quantification and abstention for machine learning is provided and how these techniques could improve the safety and reliability of current ML systems being used in healthcare settings are highlighted.

Abstract

There is great excitement that medical artificial intelligence (AI) based on machine learning (ML) can be used to improve decision making at the patient level in a variety of healthcare settings. However, the quantification and communication of uncertainty for individual predictions is often neglected even though uncertainty estimates could lead to more principled decision-making and enable machine learning models to automatically or semi-automatically abstain on samples for which there is high uncertainty. In this article, we provide an overview of different approaches to uncertainty quantification and abstention for machine learning and highlight how these techniques could improve the safety and reliability of current ML systems being used in healthcare settings. Effective quantification and communication of uncertainty could help to engender trust with healthcare workers, while providing safeguards against known failure modes of current machine learning approaches. As machine learning becomes further integrated into healthcare environments, the ability to say “I’m not sure” or “I don’t know” when uncertain is a necessary capability to enable safe clinical deployment.

Authors

Benjamin Kompa

1 papers

Jasper Snoek

8 papers

A. Beam

1 papers

References75 items

The Convergence of Human and Artificial Intelligence on Clinical Care - Part I

Consistent Estimators for Learning to Defer to an Expert

A Human-Centered Evaluation of a Deep Learning System Deployed in Clinics for the Detection of Diabetic Retinopathy

Clinical prediction models

BatchEnsemble: An Alternative Approach to Efficient Ensemble and Lifelong Learning

Second opinion needed: communicating uncertainty in medical machine learning

Published in

npj Digital Medicine(2021)

External Links:

Generate Graph DownloadPDF

TL;DR

Abstract

Authors

Benjamin Kompa

1 papers

Jasper Snoek

8 papers

A. Beam

1 papers

References75 items

The Convergence of Human and Artificial Intelligence on Clinical Care - Part I

Consistent Estimators for Learning to Defer to an Expert

A Human-Centered Evaluation of a Deep Learning System Deployed in Clinics for the Detection of Diabetic Retinopathy

Clinical prediction models

BatchEnsemble: An Alternative Approach to Efficient Ensemble and Lifelong Learning

Pitfalls of In-Domain Uncertainty Estimation and Ensembling in Deep Learning

The Case for Bayesian Deep Learning

Challenges to the Reproducibility of Machine Learning Models in Health Care.

Deep Gamblers: Learning to Abstain with Portfolio Theory

Quality of Uncertainty Quantification for Bayesian Neural Network Inference

Analyzing the role of model uncertainty for electronic health records

Can You Trust Your Model's Uncertainty? Evaluating Predictive Uncertainty Under Dataset Shift

Reporting of artificial intelligence prediction models

Conformal Prediction Under Covariate Shift

Adversarial attacks on medical machine learning

The limits of distribution-free conditional predictive inference

A systematic review shows no performance benefit of machine learning over logistic regression for clinical prediction models.

TL;DR

Abstract

Authors

References75 items

The Convergence of Human and Artificial Intelligence on Clinical Care - Part I

Consistent Estimators for Learning to Defer to an Expert

A Human-Centered Evaluation of a Deep Learning System Deployed in Clinics for the Detection of Diabetic Retinopathy

Clinical prediction models

BatchEnsemble: An Alternative Approach to Efficient Ensemble and Lifelong Learning

TL;DR

Abstract

Authors

References75 items

The Convergence of Human and Artificial Intelligence on Clinical Care - Part I

Consistent Estimators for Learning to Defer to an Expert

A Human-Centered Evaluation of a Deep Learning System Deployed in Clinics for the Detection of Diabetic Retinopathy

Clinical prediction models

BatchEnsemble: An Alternative Approach to Efficient Ensemble and Lifelong Learning

Pitfalls of In-Domain Uncertainty Estimation and Ensembling in Deep Learning

The Case for Bayesian Deep Learning

Challenges to the Reproducibility of Machine Learning Models in Health Care.

Deep Gamblers: Learning to Abstain with Portfolio Theory

Quality of Uncertainty Quantification for Bayesian Neural Network Inference

Analyzing the role of model uncertainty for electronic health records

Can You Trust Your Model's Uncertainty? Evaluating Predictive Uncertainty Under Dataset Shift

Reporting of artificial intelligence prediction models

Conformal Prediction Under Covariate Shift

Adversarial attacks on medical machine learning

The limits of distribution-free conditional predictive inference

A systematic review shows no performance benefit of machine learning over logistic regression for clinical prediction models.

SelectiveNet: A Deep Neural Network with an Integrated Reject Option

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Reprints and permission information

Appendix for: How Good is the Bayes Posterior in Deep Neural Networks Really?

the 2020 CHI Conference on Human Factors in Computing Systems 1 – 12

BatchEnsemble: Efficient Ensemble of Deep Neural Networks via Rank-1 Perturbation

Consistent algorithms for multiclass classification with an abstain option

Arti ﬁ cial intelligence in healthcare

Uncertainty in Deep Learning