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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Main Authors: Christine M. Cutillo, Karlie R. Sharma, Luca Foschini, Shinjini Kundu, Maxine Mackintosh, Kenneth D. Mandl, MI in Healthcare Workshop Working Group
Format: Article
Language:English
Published: Nature Publishing Group 2020-03-01
Series:npj Digital Medicine
Online Access:https://doi.org/10.1038/s41746-020-0254-2
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spelling doaj-83ce70f92f1146a5ac7b2a83189e07912021-03-28T11:39:18ZengNature Publishing Groupnpj Digital Medicine2398-63522020-03-01311510.1038/s41746-020-0254-2Machine intelligence in healthcare—perspectives on trustworthiness, explainability, usability, and transparencyChristine M. Cutillo0Karlie R. Sharma1Luca Foschini2Shinjini Kundu3Maxine Mackintosh4Kenneth D. Mandl5MI in Healthcare Workshop Working GroupNational Center for Advancing Translational Sciences, National Institutes of HealthNational Center for Advancing Translational Sciences, National Institutes of HealthEvidation Health Inc.Department of Radiology, The Johns Hopkins HospitalUniversity College LondonComputational Health Informatics Program, Boston Children’s HospitalMachine 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.https://doi.org/10.1038/s41746-020-0254-2
collection DOAJ
language English
format Article
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author Christine M. Cutillo
Karlie R. Sharma
Luca Foschini
Shinjini Kundu
Maxine Mackintosh
Kenneth D. Mandl
MI in Healthcare Workshop Working Group
spellingShingle 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
npj Digital Medicine
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 Publishing Group
series npj Digital Medicine
issn 2398-6352
publishDate 2020-03-01
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.
url https://doi.org/10.1038/s41746-020-0254-2
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