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Industry ties and evidence in public comments on the FDA framework for modifications to artificial intelligence/machine learning-based medical devices: a cross sectional study

OBJECTIVES: To determine the extent and disclosure of financial ties to industry and use of scientific evidence in comments on a US Food and Drug Administration (FDA) regulatory framework for modifications to artificial intelligence/machine learning (AI/ML)-based software as a medical device (SaMD)....

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Autores principales: Smith, James Andrew, Abhari, Roxanna E, Hussain, Zain, Heneghan, Carl, Collins, Gary S, Carr, Andrew J
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BMJ Publishing Group 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7559037/
https://www.ncbi.nlm.nih.gov/pubmed/33055121
http://dx.doi.org/10.1136/bmjopen-2020-039969
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author Smith, James Andrew
Abhari, Roxanna E
Hussain, Zain
Heneghan, Carl
Collins, Gary S
Carr, Andrew J
author_facet Smith, James Andrew
Abhari, Roxanna E
Hussain, Zain
Heneghan, Carl
Collins, Gary S
Carr, Andrew J
author_sort Smith, James Andrew
collection PubMed
description OBJECTIVES: To determine the extent and disclosure of financial ties to industry and use of scientific evidence in comments on a US Food and Drug Administration (FDA) regulatory framework for modifications to artificial intelligence/machine learning (AI/ML)-based software as a medical device (SaMD). DESIGN: Cross-sectional study. SETTING: We searched all publicly available comments on the FDA ‘Proposed Regulatory Framework for Modifications to Artificial Intelligence/Machine Learning (AI/ML)-Based Software as a Medical Device (SaMD)—Discussion Paper and Request for Feedback’ from 2 April 2019 to 8 August 2019. MAIN OUTCOME MEASURES: The proportion of articles submitted by parties with financial ties to industry, disclosing those ties, citing scientific articles, citing systematic reviews and meta-analyses, and using a systematic process to identify relevant literature. RESULTS: We analysed 125 comments submitted on the proposed framework. 79 (63%) comments came from parties with financial ties; for 36 (29%) comments, it was not clear and the absence of financial ties could only be confirmed for 10 (8%) comments. No financial ties were disclosed in any of the comments that were not from industry submitters. The vast majority of submitted comments (86%) did not cite any scientific literature, just 4% cited a systematic review or meta-analysis and no comments indicated that a systematic process was used to identify relevant literature. CONCLUSIONS: Financial ties to industry were common and undisclosed, and scientific evidence, including systematic reviews and meta-analyses, were rarely cited. To ensure regulatory frameworks best serve patient interests, the FDA should mandate disclosure of potential conflicts of interest (including financial ties) in comments, encourage the use of scientific evidence, and encourage engagement from non-conflicted parties.
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spelling pubmed-75590372020-10-19 Industry ties and evidence in public comments on the FDA framework for modifications to artificial intelligence/machine learning-based medical devices: a cross sectional study Smith, James Andrew Abhari, Roxanna E Hussain, Zain Heneghan, Carl Collins, Gary S Carr, Andrew J BMJ Open Health Policy OBJECTIVES: To determine the extent and disclosure of financial ties to industry and use of scientific evidence in comments on a US Food and Drug Administration (FDA) regulatory framework for modifications to artificial intelligence/machine learning (AI/ML)-based software as a medical device (SaMD). DESIGN: Cross-sectional study. SETTING: We searched all publicly available comments on the FDA ‘Proposed Regulatory Framework for Modifications to Artificial Intelligence/Machine Learning (AI/ML)-Based Software as a Medical Device (SaMD)—Discussion Paper and Request for Feedback’ from 2 April 2019 to 8 August 2019. MAIN OUTCOME MEASURES: The proportion of articles submitted by parties with financial ties to industry, disclosing those ties, citing scientific articles, citing systematic reviews and meta-analyses, and using a systematic process to identify relevant literature. RESULTS: We analysed 125 comments submitted on the proposed framework. 79 (63%) comments came from parties with financial ties; for 36 (29%) comments, it was not clear and the absence of financial ties could only be confirmed for 10 (8%) comments. No financial ties were disclosed in any of the comments that were not from industry submitters. The vast majority of submitted comments (86%) did not cite any scientific literature, just 4% cited a systematic review or meta-analysis and no comments indicated that a systematic process was used to identify relevant literature. CONCLUSIONS: Financial ties to industry were common and undisclosed, and scientific evidence, including systematic reviews and meta-analyses, were rarely cited. To ensure regulatory frameworks best serve patient interests, the FDA should mandate disclosure of potential conflicts of interest (including financial ties) in comments, encourage the use of scientific evidence, and encourage engagement from non-conflicted parties. BMJ Publishing Group 2020-10-14 /pmc/articles/PMC7559037/ /pubmed/33055121 http://dx.doi.org/10.1136/bmjopen-2020-039969 Text en © Author(s) (or their employer(s)) 2020. Re-use permitted under CC BY. Published by BMJ. https://creativecommons.org/licenses/by/4.0/ https://creativecommons.org/licenses/by/4.0/This is an open access article distributed in accordance with the Creative Commons Attribution 4.0 Unported (CC BY 4.0) license, which permits others to copy, redistribute, remix, transform and build upon this work for any purpose, provided the original work is properly cited, a link to the licence is given, and indication of whether changes were made. See: https://creativecommons.org/licenses/by/4.0/.
spellingShingle Health Policy
Smith, James Andrew
Abhari, Roxanna E
Hussain, Zain
Heneghan, Carl
Collins, Gary S
Carr, Andrew J
Industry ties and evidence in public comments on the FDA framework for modifications to artificial intelligence/machine learning-based medical devices: a cross sectional study
title Industry ties and evidence in public comments on the FDA framework for modifications to artificial intelligence/machine learning-based medical devices: a cross sectional study
title_full Industry ties and evidence in public comments on the FDA framework for modifications to artificial intelligence/machine learning-based medical devices: a cross sectional study
title_fullStr Industry ties and evidence in public comments on the FDA framework for modifications to artificial intelligence/machine learning-based medical devices: a cross sectional study
title_full_unstemmed Industry ties and evidence in public comments on the FDA framework for modifications to artificial intelligence/machine learning-based medical devices: a cross sectional study
title_short Industry ties and evidence in public comments on the FDA framework for modifications to artificial intelligence/machine learning-based medical devices: a cross sectional study
title_sort industry ties and evidence in public comments on the fda framework for modifications to artificial intelligence/machine learning-based medical devices: a cross sectional study
topic Health Policy
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7559037/
https://www.ncbi.nlm.nih.gov/pubmed/33055121
http://dx.doi.org/10.1136/bmjopen-2020-039969
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