Reporting guidelines for clinical trial reports for interventions involving artificial intelligence: the CONSORT-AI extension X Liu, SC Rivera, D Moher, MJ Calvert, AK Denniston bmj 370, 2020 | 261 | 2020 |
Hidden stratification causes clinically meaningful failures in machine learning for medical imaging L Oakden-Rayner, J Dunnmon, G Carneiro, C Ré Proceedings of the ACM conference on health, inference, and learning, 151-159, 2020 | 157 | 2020 |
Precision radiology: predicting longevity using feature engineering and deep learning methods in a radiomics framework L Oakden-Rayner, G Carneiro, T Bessen, JC Nascimento, AP Bradley, ... Scientific reports 7 (1), 1-13, 2017 | 124 | 2017 |
Deep learning predicts hip fracture using confounding patient and healthcare variables MA Badgeley, JR Zech, L Oakden-Rayner, BS Glicksberg, M Liu, W Gale, ... NPJ digital medicine 2 (1), 1-10, 2019 | 114 | 2019 |
Exploring large-scale public medical image datasets L Oakden-Rayner Academic radiology 27 (1), 106-112, 2020 | 93 | 2020 |
Detecting hip fractures with radiologist-level performance using deep neural networks W Gale, L Oakden-Rayner, G Carneiro, AP Bradley, LJ Palmer arXiv preprint arXiv:1711.06504, 2017 | 83 | 2017 |
Guidelines for clinical trial protocols for interventions involving artificial intelligence: the SPIRIT-AI extension SC Rivera, X Liu, AW Chan, AK Denniston, MJ Calvert Bmj 370, 2020 | 82 | 2020 |
Producing Radiologist-Quality Reports for Interpretable Deep Learning. W Gale, L Oakden-Rayner, G Carneiro, LJ Palmer, AP Bradley 2019 IEEE 16th international symposium on biomedical imaging (ISBI 2019 …, 2019 | 49* | 2019 |
Exploring the ChestXray14 dataset: problems L Oakden-Rayner Wordpress: Luke Oakden Rayner, 2017 | 48 | 2017 |
The false hope of current approaches to explainable artificial intelligence in health care M Ghassemi, L Oakden-Rayner, AL Beam The Lancet Digital Health 3 (11), e745-e750, 2021 | 46 | 2021 |
Deep learning natural language processing successfully predicts the cerebrovascular cause of transient ischemic attack-like presentations S Bacchi, L Oakden-Rayner, T Zerner, T Kleinig, S Patel, J Jannes Stroke 50 (3), 758-760, 2019 | 33 | 2019 |
Deep learning in the prediction of ischaemic stroke thrombolysis functional outcomes: a pilot study S Bacchi, T Zerner, L Oakden-Rayner, T Kleinig, S Patel, J Jannes Academic radiology 27 (2), e19-e23, 2020 | 29 | 2020 |
Reading Race: AI Recognises Patient's Racial Identity In Medical Images I Banerjee, AR Bhimireddy, JL Burns, LA Celi, LC Chen, R Correa, ... arXiv preprint arXiv:2107.10356, 2021 | 23 | 2021 |
Automated 5-year mortality prediction using deep learning and radiomics features from chest computed tomography G Carneiro, L Oakden-Rayner, AP Bradley, J Nascimento, L Palmer 2017 IEEE 14th International Symposium on Biomedical Imaging (ISBI 2017 …, 2017 | 23 | 2017 |
The rebirth of CAD: how is modern AI different from the CAD we know? L Oakden-Rayner Radiology: artificial intelligence 1 (3), e180089, 2019 | 21 | 2019 |
A survey of clinicians on the use of artificial intelligence in ophthalmology, dermatology, radiology and radiation oncology J Scheetz, P Rothschild, M McGuinness, X Hadoux, HP Soyer, M Janda, ... Scientific reports 11 (1), 1-10, 2021 | 19 | 2021 |
CheXNet: an in-depth review L Oakden-Rayner URL: https://lukeoakdenrayner. wordpress. com/2018/01/24/chexnetan-in-depth …, 2018 | 19 | 2018 |
Towards generative adversarial networks as a new paradigm for radiology education SG Finlayson, H Lee, IS Kohane, L Oakden-Rayner arXiv preprint arXiv:1812.01547, 2018 | 17 | 2018 |
Medical journals should embrace preprints to address the reproducibility crisis L Oakden-Rayner, AL Beam, LJ Palmer International Journal of Epidemiology 47 (5), 1363-1365, 2018 | 16 | 2018 |
Effect of a comprehensive deep-learning model on the accuracy of chest x-ray interpretation by radiologists: a retrospective, multireader multicase study JCY Seah, CHM Tang, QD Buchlak, XG Holt, JB Wardman, A Aimoldin, ... The Lancet Digital Health 3 (8), e496-e506, 2021 | 13 | 2021 |