Machine intelligence in healthcare—perspectives on trustworthiness, explainability, usability, and transparency

Machine Intelligence (MI) is rapidly becoming an important approach across biomedical discovery, clinical research, medical diagnostics/devices, and precision medicine. Such tools can uncover new possibilities for researchers, physicians, and patients, allowing them to make more informed decisions a...

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Autores principales:	Christine M. Cutillo, Karlie R. Sharma, Luca Foschini, Shinjini Kundu, Maxine Mackintosh, Kenneth D. Mandl, MI in Healthcare Workshop Working Group
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2020
Materias:	Computer applications to medicine. Medical informatics R858-859.7
Acceso en línea:	https://doaj.org/article/83ce70f92f1146a5ac7b2a83189e0791
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spelling	oai:doaj.org-article:83ce70f92f1146a5ac7b2a83189e07912021-12-02T16:36:05ZMachine intelligence in healthcare—perspectives on trustworthiness, explainability, usability, and transparency10.1038/s41746-020-0254-22398-6352https://doaj.org/article/83ce70f92f1146a5ac7b2a83189e07912020-03-01T00:00:00Zhttps://doi.org/10.1038/s41746-020-0254-2https://doaj.org/toc/2398-6352Machine Intelligence (MI) is rapidly becoming an important approach across biomedical discovery, clinical research, medical diagnostics/devices, and precision medicine. Such tools can uncover new possibilities for researchers, physicians, and patients, allowing them to make more informed decisions and achieve better outcomes. When deployed in healthcare settings, these approaches have the potential to enhance efficiency and effectiveness of the health research and care ecosystem, and ultimately improve quality of patient care. In response to the increased use of MI in healthcare, and issues associated when applying such approaches to clinical care settings, the National Institutes of Health (NIH) and National Center for Advancing Translational Sciences (NCATS) co-hosted a Machine Intelligence in Healthcare workshop with the National Cancer Institute (NCI) and the National Institute of Biomedical Imaging and Bioengineering (NIBIB) on 12 July 2019. Speakers and attendees included researchers, clinicians and patients/ patient advocates, with representation from industry, academia, and federal agencies. A number of issues were addressed, including: data quality and quantity; access and use of electronic health records (EHRs); transparency and explainability of the system in contrast to the entire clinical workflow; and the impact of bias on system outputs, among other topics. This whitepaper reports on key issues associated with MI specific to applications in the healthcare field, identifies areas of improvement for MI systems in the context of healthcare, and proposes avenues and solutions for these issues, with the aim of surfacing key areas that, if appropriately addressed, could accelerate progress in the field effectively, transparently, and ethically.Christine M. CutilloKarlie R. SharmaLuca FoschiniShinjini KunduMaxine MackintoshKenneth D. MandlMI in Healthcare Workshop Working GroupNature PortfolioarticleComputer applications to medicine. Medical informaticsR858-859.7ENnpj Digital Medicine, Vol 3, Iss 1, Pp 1-5 (2020)
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 Christine M. Cutillo Karlie R. Sharma Luca Foschini Shinjini Kundu Maxine Mackintosh Kenneth D. Mandl MI in Healthcare Workshop Working Group Machine intelligence in healthcare—perspectives on trustworthiness, explainability, usability, and transparency
description	Machine Intelligence (MI) is rapidly becoming an important approach across biomedical discovery, clinical research, medical diagnostics/devices, and precision medicine. Such tools can uncover new possibilities for researchers, physicians, and patients, allowing them to make more informed decisions and achieve better outcomes. When deployed in healthcare settings, these approaches have the potential to enhance efficiency and effectiveness of the health research and care ecosystem, and ultimately improve quality of patient care. In response to the increased use of MI in healthcare, and issues associated when applying such approaches to clinical care settings, the National Institutes of Health (NIH) and National Center for Advancing Translational Sciences (NCATS) co-hosted a Machine Intelligence in Healthcare workshop with the National Cancer Institute (NCI) and the National Institute of Biomedical Imaging and Bioengineering (NIBIB) on 12 July 2019. Speakers and attendees included researchers, clinicians and patients/ patient advocates, with representation from industry, academia, and federal agencies. A number of issues were addressed, including: data quality and quantity; access and use of electronic health records (EHRs); transparency and explainability of the system in contrast to the entire clinical workflow; and the impact of bias on system outputs, among other topics. This whitepaper reports on key issues associated with MI specific to applications in the healthcare field, identifies areas of improvement for MI systems in the context of healthcare, and proposes avenues and solutions for these issues, with the aim of surfacing key areas that, if appropriately addressed, could accelerate progress in the field effectively, transparently, and ethically.
format	article
author	Christine M. Cutillo Karlie R. Sharma Luca Foschini Shinjini Kundu Maxine Mackintosh Kenneth D. Mandl MI in Healthcare Workshop Working Group
author_facet	Christine M. Cutillo Karlie R. Sharma Luca Foschini Shinjini Kundu Maxine Mackintosh Kenneth D. Mandl MI in Healthcare Workshop Working Group
author_sort	Christine M. Cutillo
title	Machine intelligence in healthcare—perspectives on trustworthiness, explainability, usability, and transparency
title_short	Machine intelligence in healthcare—perspectives on trustworthiness, explainability, usability, and transparency
title_full	Machine intelligence in healthcare—perspectives on trustworthiness, explainability, usability, and transparency
title_fullStr	Machine intelligence in healthcare—perspectives on trustworthiness, explainability, usability, and transparency
title_full_unstemmed	Machine intelligence in healthcare—perspectives on trustworthiness, explainability, usability, and transparency
title_sort	machine intelligence in healthcare—perspectives on trustworthiness, explainability, usability, and transparency
publisher	Nature Portfolio
publishDate	2020
url	https://doaj.org/article/83ce70f92f1146a5ac7b2a83189e0791
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Machine intelligence in healthcare—perspectives on trustworthiness, explainability, usability, and transparency

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