scholarly journals 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

Author(s):  
James A Smith ◽  
Roxanna E Abhari ◽  
Zain U Hussain ◽  
Carl Heneghan ◽  
Gary S Collins ◽  
...  

AbstractObjectivesTo 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).DesignCross-sectional study.SettingWe 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 April 2nd 2019 to August 8th 2019.Main outcome measuresThe 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.ResultsWe 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.ConclusionsFinancial 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.Strengths and limitations of this study-We analysed the extent of financial ties to industry and the use of scientific evidence in comments on the proposed FDA framework-We used a comprehensive strategy to attempt to identify financial ties to industry-Readers may be able to contribute higher quality comments to subsequent drafts of this framework-There is heterogeneity in the degree of conflict with respect to the framework that the recorded financial ties may represent; some ties will be more likely than others to result in biased commenting-Because the framework could not be classified as pro-industry or not, we did not classify the direction of opinions expressed in comments with respect to the framework and their association with financial ties-We do not know how information submitted to FDA is used internally in the rule-making process

BMJ Open ◽  
2020 ◽  
Vol 10 (10) ◽  
pp. e039969 ◽  
Author(s):  
James Andrew Smith ◽  
Roxanna E Abhari ◽  
Zain Hussain ◽  
Carl Heneghan ◽  
Gary S Collins ◽  
...  

ObjectivesTo 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).DesignCross-sectional study.SettingWe 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 measuresThe 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.ResultsWe 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.ConclusionsFinancial 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.


2019 ◽  
Vol 19 (1) ◽  
Author(s):  
Hoang Thi Nam Giang ◽  
Ali Mahmoud Ahmed ◽  
Reem Yousry Fala ◽  
Mohamed Magdy Khattab ◽  
Mona Hassan Ahmed Othman ◽  
...  

BMJ Open ◽  
2021 ◽  
Vol 11 (6) ◽  
pp. e046265
Author(s):  
Shotaro Doki ◽  
Shinichiro Sasahara ◽  
Daisuke Hori ◽  
Yuichi Oi ◽  
Tsukasa Takahashi ◽  
...  

ObjectivesPsychological distress is a worldwide problem and a serious problem that needs to be addressed in the field of occupational health. This study aimed to use artificial intelligence (AI) to predict psychological distress among workers using sociodemographic, lifestyle and sleep factors, not subjective information such as mood and emotion, and to examine the performance of the AI models through a comparison with psychiatrists.DesignCross-sectional study.SettingWe conducted a survey on psychological distress and living conditions among workers. An AI model for predicting psychological distress was created and then the results were compared in terms of accuracy with predictions made by psychiatrists.ParticipantsAn AI model of the neural network and six psychiatrists.Primary outcomeThe accuracies of the AI model and psychiatrists for predicting psychological distress.MethodsIn total, data from 7251 workers were analysed to predict moderate and severe psychological distress. An AI model of the neural network was created and accuracy, sensitivity and specificity were calculated. Six psychiatrists used the same data as the AI model to predict psychological distress and conduct a comparison with the AI model.ResultsThe accuracies of the AI model and psychiatrists for predicting moderate psychological distress were 65.2% and 64.4%, respectively, showing no significant difference. The accuracies of the AI model and psychiatrists for predicting severe psychological distress were 89.9% and 85.5%, respectively, indicating that the AI model had significantly higher accuracy.ConclusionsA machine learning model was successfully developed to screen workers with depressed mood. The explanatory variables used for the predictions did not directly ask about mood. Therefore, this newly developed model appears to be able to predict psychological distress among workers easily, regardless of their subjective views.


2017 ◽  
Vol 27 (6) ◽  
pp. 619-627 ◽  
Author(s):  
V. C. H. Chung ◽  
X. Y. Wu ◽  
Y. Feng ◽  
R. S. T. Ho ◽  
S. Y. S. Wong ◽  
...  

Aims.Depression is one of the most common mental disorders and identifying effective treatment strategies is crucial for the control of depression. Well-conducted systematic reviews (SRs) and meta-analyses can provide the best evidence for supporting treatment decision-making. Nevertheless, the trustworthiness of conclusions can be limited by lack of methodological rigour. This study aims to assess the methodological quality of a representative sample of SRs on depression treatments.Methods.A cross-sectional study on the bibliographical and methodological characteristics of SRs published on depression treatments trials was conducted. Two electronic databases (the Cochrane Database of Systematic Reviews and the Database of Abstracts of Reviews of Effects) were searched for potential SRs. SRs with at least one meta-analysis on the effects of depression treatments were considered eligible. The methodological quality of included SRs was assessed using the validated AMSTAR (Assessing the Methodological Quality of Systematic Reviews) tool. The associations between bibliographical characteristics and scoring on AMSTAR items were analysed using logistic regression analysis.Results.A total of 358 SRs were included and appraised. Over half of included SRs (n = 195) focused on non-pharmacological treatments and harms were reported in 45.5% (n = 163) of all studies. Studies varied in methods and reporting practices: only 112 (31.3%) took the risk of bias among primary studies into account when formulating conclusions; 245 (68.4%) did not fully declare conflict of interests; 93 (26.0%) reported an ‘a priori’ design and 104 (29.1%) provided lists of both included and excluded studies. Results from regression analyses showed: more recent publications were more likely to report ‘a priori’ designs [adjusted odds ratio (AOR) 1.31, 95% confidence interval (CI) 1.09–1.57], to describe study characteristics fully (AOR 1.16, 95% CI 1.06–1.28), and to assess presence of publication bias (AOR 1.13, 95% CI 1.06–1.19), but were less likely to list both included and excluded studies (AOR 0.86, 95% CI 0.81–0.92). SRs published in journals with higher impact factor (AOR 1.14, 95% CI 1.04–1.25), completed by more review authors (AOR 1.12, 95% CI 1.01–1.24) and SRs on non-pharmacological treatments (AOR 1.62, 95% CI 1.01–2.59) were associated with better performance in publication bias assessment.Conclusion.The methodological quality of included SRs is disappointing. Future SRs should strive to improve rigour by considering of risk of bias when formulating conclusions, reporting conflict of interests and authors should explicitly describe harms. SR authors should also use appropriate methods to combine the results, prevent language and publication biases, and ensure timely updates.


Heliyon ◽  
2020 ◽  
Vol 6 (9) ◽  
pp. e04776
Author(s):  
Katja Matthias ◽  
Olesja Rissling ◽  
Dawid Pieper ◽  
Johannes Morche ◽  
Marc Nocon ◽  
...  

Bone ◽  
2020 ◽  
Vol 139 ◽  
pp. 115541
Author(s):  
Anna K.N. Tsoi ◽  
Leonard T.F. Ho ◽  
Irene X.Y. Wu ◽  
Charlene H.L. Wong ◽  
Robin S.T. Ho ◽  
...  

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