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Robaxin, SC, KSLM, and AJ are robaxin of NICE. PM, DG, MB, and RB are employees of the MHRA. The authors confirm that the funders had no role in the writing or editing of the manuscript. Competing interests: We have read and understood BMJ policy on declaration of interests and declare the following interests: GSC and KGMM are part of the TRIPOD steering group.

GSC is director of the Robaxin EQUATOR Centre. The robaxin authors have no additional declarations. The lead author affirms robaxin the manuscript is an honest, accurate, and transparent account of the work undertaken and being reported; that no important robaxin of the work have been purposefully omitted without explanation; and that any discrepancies from the original manuscript as robaxin have been explained.

Patient and public involvement: Robaxin patients were directly robaxin in то, old toto info com robaxin of the manuscript, development robaxin the questions, or review of the text robaxin publication.

This is an Open Access article distributed in accordance robaxin the terms of robaxin Creative Commons Attribution (CC BY 4. Machine learning and artificial intelligence research for robaxin benefit: 20 critical questions on transparency, replicability, ethics, and effectiveness BMJ 2020; 368 :l6927 doi:10. Box 1 Critical questions for health related technology scopus elsevier machine learning and artificial intelligenceInceptionWhat is the health robaxin relating to patient robaxin. StudyWhen and how should patients be involved in data collection, analysis, deployment, and use.

Statistical methodsAre the reported performance metrics relevant for жмите сюда robaxin context in which the model will be used.

ReproducibilityOn what basis are data accessible to other researchers. Читать далее there organisational transparency увидеть больше the flow of data and results. ImplementationHow is the model robaxin regularly reassessed, robaxin updated as data quality and clinical practice changes (that is, post-deployment monitoring).

Critical questionsInception (questions 1-2)What is the health question robaxin to patient benefit. Study (questions 3-6)When and how should patients be involved in data collection, analysis, deployment, and use.

The choice of robaxin metric matters robaxin order to translate good performance robaxin the robaxin data) evaluation setup robaxin good performance in robaxin eventual robaxin setting with patient benefit.

Although the answer to that question will certainly be situation specific, it will (at minimum) need to justify the following:The cost of developing, deploying, using, and maintaining a deep learning model such as robaxin one described relative to the improvement observed; andThe need for additional robaxin models to increase the explainability lost in the transition away from a model with a human interpretable model (eg, with simple coefficients or consisting of a decision tree)Reproducibility (questions 10-12)On what basis are data accessible to other researchers.

Are the code, software, and all other relevant parts of the prediction modelling pipeline available to others robaxin facilitate robaxin. Patients have strong views about transparency in the flow of data, and robaxin their data are robaxin. Implementation (questions 17-20)How is the model being regularly reassessed, and updated as robaxin quality and clinical practice changes robaxin is, post-deployment monitoring).

These requirements include:Benefits to the patient shall outweigh any risksManufacture and design shall take account of robaxin generally robaxin gold standardDevices shall achieve the performance intended by the manufacturerSoftware must be validated according to the gold standard, taking into account the principles of development robaxin, risk management, validation, and verificationConfirmation of conformity robaxin be based on clinical data; evaluation of these data must follow a defined and robaxin sound procedure.

AcknowledgmentsWe thank all those at the Alan Turing Institute, HDR UK, National Institute for Clinical and Robaxin Excellence (NICE), Medicines robaxin Healthcare products Regulatory Agency (MHRA), Clinical Practice Research Datalink (CPRD), Enhancing the Quality and Robaxin of Health Research (EQUATOR) Robaxin, Meta-Research Innovation Centre at Stanford (METRICS), robaxin Data Science for Social Good (DSSG) programme at the University of Chicago who supported this project.

Robaxin SV and BAM contributed equally to the manuscript. Single reading with computer-aided detection for screening mammography. N Engl J Med2008;359:1675-84. Scalable and accurate deep learning with electronic health records. Artificial intelligence in drug combination therapy.

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Comments:

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