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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.

Original publication

DOI

10.1136/bmjopen-2020-039969

Type

Journal article

Journal

Bmj open

Publication Date

14/10/2020

Volume

10

Keywords

health policy, medical law, public health, Artificial Intelligence, Conflict of Interest, Cross-Sectional Studies, Disclosure, Drug Industry, Humans, Machine Learning, United States, United States Food and Drug Administration