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Colorectal cancer
BMJ, The - Tập 346 Số may20 7 - Trang f3172-f3172 - 2013
William Hamilton, M. G. Coleman, Greg Rubin
Multidisciplinary biopsychosocial rehabilitation for chronic low back pain: Cochrane systematic review and meta-analysis
BMJ, The - Tập 350 Số feb18 5 - Trang h444-h444 - 2015
Steven J. Kamper, Adri T. Apeldoorn, Alessandro Chiarotto, Rob Smeets, Raymond Ostelo, Jaime Guzmán, Maurits W. van Tulder
Efficacy of vitamin and antioxidant supplements in prevention of cardiovascular disease: systematic review and meta-analysis of randomised controlled trials
BMJ, The - Tập 346 Số jan18 1 - Trang f10-f10 - 2013
Seung‐Kwon Myung, Woong Ju, B. Cho, Seung‐Won Oh, So-Myoung Park, Bon‐Kwon Koo, Byung‐Joo Park
Prediction models for cardiovascular disease risk in the general population: systematic review
BMJ, The - - Trang i2416
Johanna AAG Damen, Lotty Hooft, Ewoud Schuit, Thomas P. A. Debray, Gary S. Collins, Ioanna Tzoulaki, Camille Lassale, George C.M. Siontis, Virginia Chiocchia, Corran Roberts, Michael Maia Schlüssel, Stephen Gerry, James A Black, Pauline Heus, Yvonne T. van der Schouw, Linda M. Peelen, Karel G.M. Moons
Lister restaged: one sure step for science and safer surgery
BMJ, The - Tập 347 Số oct09 4 - Trang f6075-f6075 - 2013
Ben Chisnall
Benzodiazepine use and risk of Alzheimer's disease: case-control study
BMJ, The - Tập 349 Số sep09 2 - Trang g5205-g5205 - 2014
Sophie Billioti de Gage, Yola Moride, Thiérry Ducruet, Tobias Kurth, Hélène Verdoux, Marie Tournier, Alexandre Pariente, Bernard Bégaud
Corruption: medicine's dirty open secret
BMJ, The - Tập 348 Số jun25 9 - Trang g4184-g4184 - 2014
A. Jain, Samiran Nundy, Kamran Abbasi
Comparison of the two most commonly used treatments for pyoderma gangrenosum: results of the STOP GAP randomised controlled trial
BMJ, The - Tập 350 Số jun12 3 - Trang h2958-h2958 - 2015
Anthony D. Ormerod, Kim S Thomas, Fiona E. Craig, Eleanor Mitchell, Nicola Greenlaw, John Norrie, James Mason, Shernaz Walton, G.A. Johnston, Hywel C Williams
Developing and evaluating complex interventions: the new Medical Research Council guidance
BMJ, The - - Trang a1655
Peter Craig, Paul Dieppe, Sally MacIntyre, Susan Michie, Irwin Nazareth, Mark Petticrew
Use of artificial intelligence for image analysis in breast cancer screening programmes: systematic review of test accuracy
BMJ, The - - Trang n1872
Karoline Freeman, Julia Geppert, Chris Stinton, Daniel Todkill, Samantha Johnson, Aileen Clarke, Sian Taylor‐Phillips
Abstract Objective To examine the accuracy of artificial intelligence (AI) for the detection of breast cancer in mammography screening practice. Design Systematic review of test accuracy studies. Data sources Medline, Embase, Web of Science, and Cochrane Database of Systematic Reviews from 1 January 2010 to 17 May 2021. Eligibility criteria Studies reporting test accuracy of AI algorithms, alone or in combination with radiologists, to detect cancer in women’s digital mammograms in screening practice, or in test sets. Reference standard was biopsy with histology or follow-up (for screen negative women). Outcomes included test accuracy and cancer type detected. Study selection and synthesis Two reviewers independently assessed articles for inclusion and assessed the methodological quality of included studies using the QUality Assessment of Diagnostic Accuracy Studies-2 (QUADAS-2) tool. A single reviewer extracted data, which were checked by a second reviewer. Narrative data synthesis was performed. Results Twelve studies totalling 131 822 screened women were included. No prospective studies measuring test accuracy of AI in screening practice were found. Studies were of poor methodological quality. Three retrospective studies compared AI systems with the clinical decisions of the original radiologist, including 79 910 women, of whom 1878 had screen detected cancer or interval cancer within 12 months of screening. Thirty four (94%) of 36 AI systems evaluated in these studies were less accurate than a single radiologist, and all were less accurate than consensus of two or more radiologists. Five smaller studies (1086 women, 520 cancers) at high risk of bias and low generalisability to the clinical context reported that all five evaluated AI systems (as standalone to replace radiologist or as a reader aid) were more accurate than a single radiologist reading a test set in the laboratory. In three studies, AI used for triage screened out 53%, 45%, and 50% of women at low risk but also 10%, 4%, and 0% of cancers detected by radiologists. Conclusions Current evidence for AI does not yet allow judgement of its accuracy in breast cancer screening programmes, and it is unclear where on the clinical pathway AI might be of most benefit. AI systems are not sufficiently specific to replace radiologist double reading in screening programmes. Promising results in smaller studies are not replicated in larger studies. Prospective studies are required to measure the effect of AI in clinical practice. Such studies will require clear stopping rules to ensure that AI does not reduce programme specificity. Study registration Protocol registered as PROSPERO CRD42020213590.
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