Second opinion needed: communicating uncertainty in medical machine learning

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 ofte...

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Autores principales:	Benjamin Kompa, Jasper Snoek, Andrew L. Beam
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Computer applications to medicine. Medical informatics R858-859.7
Acceso en línea:	https://doaj.org/article/d4f85d7dd17c413e9a36056423ac1ba9
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spelling	oai:doaj.org-article:d4f85d7dd17c413e9a36056423ac1ba92021-12-02T13:35:39ZSecond opinion needed: communicating uncertainty in medical machine learning10.1038/s41746-020-00367-32398-6352https://doaj.org/article/d4f85d7dd17c413e9a36056423ac1ba92021-01-01T00:00:00Zhttps://doi.org/10.1038/s41746-020-00367-3https://doaj.org/toc/2398-6352Abstract 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.Benjamin KompaJasper SnoekAndrew L. BeamNature PortfolioarticleComputer applications to medicine. Medical informaticsR858-859.7ENnpj Digital Medicine, Vol 4, Iss 1, Pp 1-6 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Computer applications to medicine. Medical informatics R858-859.7
spellingShingle	Computer applications to medicine. Medical informatics R858-859.7 Benjamin Kompa Jasper Snoek Andrew L. Beam Second opinion needed: communicating uncertainty in medical machine learning
description	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.
format	article
author	Benjamin Kompa Jasper Snoek Andrew L. Beam
author_facet	Benjamin Kompa Jasper Snoek Andrew L. Beam
author_sort	Benjamin Kompa
title	Second opinion needed: communicating uncertainty in medical machine learning
title_short	Second opinion needed: communicating uncertainty in medical machine learning
title_full	Second opinion needed: communicating uncertainty in medical machine learning
title_fullStr	Second opinion needed: communicating uncertainty in medical machine learning
title_full_unstemmed	Second opinion needed: communicating uncertainty in medical machine learning
title_sort	second opinion needed: communicating uncertainty in medical machine learning
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/d4f85d7dd17c413e9a36056423ac1ba9
work_keys_str_mv	AT benjaminkompa secondopinionneededcommunicatinguncertaintyinmedicalmachinelearning AT jaspersnoek secondopinionneededcommunicatinguncertaintyinmedicalmachinelearning AT andrewlbeam secondopinionneededcommunicatinguncertaintyinmedicalmachinelearning
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Second opinion needed: communicating uncertainty in medical machine learning

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