期刊
BMJ-BRITISH MEDICAL JOURNAL
卷 368, 期 -, 页码 -出版社
BMJ PUBLISHING GROUP
DOI: 10.1136/bmj.l6927
关键词
-
资金
- Engineering and Physical Sciences Research Council grant [EP/N510129/1]
- NIHR Biomedical Research Centre, Oxford
- Health Data Research UK - UK Medical Research Council [LOND1]
- Engineering and Physical Sciences Research Council
- Economic and Social Research Council
- Department of Health and Social Care (England)
- Chief Scientist Office of the Scottish Government Health and Social Care Directorates
- Health and Social Care Research and Development Division (Welsh Government)
- Public Health Agency (Northern Ireland)
- British Heart Foundation
- Wellcome Trust
- Innovative Medicines Initiative-2 Joint Undertaking (European Union's Horizon 2020 research and innovation programme) [116074]
- Innovative Medicines Initiative-2 Joint Undertaking (European Federation of Pharmaceutical Industries) [116074]
- Innovative Medicines Initiative-2 Joint Undertaking (European Society of Cardiology) [116074]
- NIHR University College London Hospitals Biomedical Research Centre
- Laura and John Arnold Foundation
- Netherlands Organisation for Health Research and Development
- University of Warwick's Impact Acceleration - EPSRC
- [TU/B/000012]
- MRC [G0902393, MR/M501633/2, MC_UP_A390_1107] Funding Source: UKRI
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.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据