Lauren Oakden-Rayner
Lauren Oakden-Rayner
Other namesLuke Oakden-Rayner
Australian Institute for Machine Learning. University of Adelaide. Royal Adelaide Hospital.
Verified email at - Homepage
Cited by
Cited by
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, H Ashrafian, ...
The Lancet Digital Health 2 (10), e537-e548, 2020
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
Guidelines for clinical trial protocols for interventions involving artificial intelligence: the SPIRIT-AI extension
SC Rivera, X Liu, AW Chan, AK Denniston, MJ Calvert, H Ashrafian, ...
The Lancet Digital Health 2 (10), e549-e560, 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
AI recognition of patient race in medical imaging: a modelling study
JW Gichoya, I Banerjee, AR Bhimireddy, JL Burns, LA Celi, LC Chen, ...
The Lancet Digital Health 4 (6), e406-e414, 2022
Reporting guideline for the early stage clinical evaluation of decision support systems driven by artificial intelligence: DECIDE-AI
B Vasey, M Nagendran, B Campbell, DA Clifton, GS Collins, S Denaxas, ...
bmj 377, 2022
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), 31, 2019
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), 1648, 2017
Exploring large-scale public medical image datasets
L Oakden-Rayner
Academic radiology 27 (1), 106-112, 2020
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), 5193, 2021
The medical algorithmic audit
X Liu, B Glocker, MM McCradden, M Ghassemi, AK Denniston, ...
The Lancet Digital Health 4 (5), e384-e397, 2022
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
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
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
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
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
Machine learning in the prediction of medical inpatient length of stay
S Bacchi, Y Tan, L Oakden‐Rayner, J Jannes, T Kleinig, S Koblar
Internal medicine journal 52 (2), 176-185, 2022
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
Exploring the ChestXray14 dataset: problems
L Oakden-Rayner
Wordpress: Luke Oakden Rayner 1 (6), 9, 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
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