| アブストラクト | 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. |
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| 投稿者 | Vollmer, Sebastian; Mateen, Bilal A; Bohner, Gergo; Kiraly, 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 |
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| 組織名 | Alan Turing Institute, Kings Cross, London, UK.;Departments of Mathematics and Statistics, University of Warwick, Coventry, UK.;Warwick Medical School, University of Warwick, Coventry, UK.;Kings College Hospital, Denmark Hill, London, UK.;Department of Statistical Science, University College London, London, UK.;University of Chicago, Chicago, IL, USA.;Science Policy and Research, National Institute for Health and Care Excellence,;Manchester, UK.;Health and Social Care Directorate, National Institute for Health and Care;Excellence, London, UK.;Data and Analytics Group, National Institute for Health and Care Excellence,;London, UK.;Clinical Practice Research Datalink, Medicines and Healthcare products Regulatory;Agency, London, UK.;Medicines and Healthcare products Regulatory Agency, London, UK.;Julius Centre for Health Sciences and Primary Care, UMC Utrecht, Utrecht;University, Utrecht, Netherlands.;UK EQUATOR Centre, Centre for Statistics in Medicine, NDORMS, University of;Oxford, Oxford, UK.;Meta-Research Innovation Centre at Stanford, Stanford University, Stanford, CA,;USA.;Alan Turing Institute, Kings Cross, London, UK cholmes@stats.ox.ac.uk.;Department of Statistics, University of Oxford, Oxford OX1 3LB, UK.;Health Data Research UK London, University College London, London, UK.;Institute of Health Informatics, University College London, London, UK.;National Institute for Health Research, University College London Hospitals;Biomedical Research Centre, University College London, London, UK. |
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