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Machine learning and artificial intelligence research for patient benefit: 20 critical questions on transparency, replicability, ethics, and effectiveness

BMJ (Online), 2020-03, Vol.368, p.l6927-l6927 [Peer Reviewed Journal]

Author(s) (or their employer(s)) 2019. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ. ;2020 Author(s) (or their employer(s)) 2019. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ. BMJ This is an Open Access article distributed in accordance with the terms of the Creative Commons Attribution (CC BY 4.0) license, which permits others to distribute, remix, adapt and build upon this work, for commercial use, provided the original work is properly cited. See: http://creativecommons.org/licenses/by/4.0/ . Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. ;ISSN: 1756-1833 ;EISSN: 1756-1833 ;DOI: 10.1136/bmj.l6927 ;PMID: 32198138

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Title:
Machine learning and artificial intelligence research for patient benefit: 20 critical questions on transparency, replicability, ethics, and effectiveness
Author: Vollmer, Sebastian ; Mateen, Bilal A ; Bohner, Gergo ; Király, Franz J ; Ghani, Rayid ; Jonsson, Pall ; Cumbers, Sarah ; Jonas, Adrian ; McAllister, Katherine S L ; Myles, Puja ; Granger, David ; Birse, Mark ; Branson, Richard ; Moons, Karel G M ; Collins, Gary S ; Ioannidis, John P A ; Holmes, Chris ; Hemingway, Harry
Subjects: Algorithms ; Artificial intelligence ; Artificial Intelligence - ethics ; Clinical medicine ; Data Collection ; Electronic health records ; Ethics ; Exploration ; Humans ; Learning algorithms ; Machine learning ; Machine Learning - ethics ; Medical ethics ; Medical research ; Patient safety ; Reproducibility of Results ; Software ; Statistical methods ; Surveys and Questionnaires
Is Part Of: BMJ (Online), 2020-03, Vol.368, p.l6927-l6927
Description: Machine learning, artificial intelligence, and other modern statistical methods are providing new opportunities to operationalise previously untapped and rapidly growing sources of data for patient benefit. Despite much promising research currently being undertaken, particularly in imaging, the literature as a whole lacks transparency, clear reporting to facilitate replicability, exploration for potential ethical concerns, and clear demonstrations of effectiveness. Among the many reasons why these problems exist, one of the most important (for which we provide a preliminary solution here) is the current lack of best practice guidance specific to machine learning and artificial intelligence. However, we believe that interdisciplinary groups pursuing research and impact projects involving machine learning and artificial intelligence for health would benefit from explicitly addressing a series of questions concerning transparency, reproducibility, ethics, and effectiveness (TREE). The 20 critical questions proposed here provide a framework for research groups to inform the design, conduct, and reporting; for editors and peer reviewers to evaluate contributions to the literature; and for patients, clinicians and policy makers to critically appraise where new findings may deliver patient benefit.
Publisher: England: BMJ Publishing Group LTD
Language: English
Identifier: ISSN: 1756-1833
EISSN: 1756-1833
DOI: 10.1136/bmj.l6927
PMID: 32198138
Source: BMJ Journals (Open Access)
MEDLINE
ProQuest Central

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Machine learning and artificial intelligence research for patient benefit: 20 critical questions on transparency, replicability, ethics, and effectiveness

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