User profiles for "author:Luke Oakden-Rayner"

Lauren Oakden-Rayner

Australian Institute for Machine Learning. University of Adelaide. Royal Adelaide Hospital.
Verified email at adelaide.edu.au
Cited by 4654

[HTML][HTML] The false hope of current approaches to explainable artificial intelligence in health care

M Ghassemi, L Oakden-Rayner… - The Lancet Digital Health, 2021 - thelancet.com
The black-box nature of current artificial intelligence (AI) has caused some to question
whether AI must be explainable to be used in high-stakes scenarios such as medicine. It has …

[HTML][HTML] Reporting guidelines for clinical trial reports for interventions involving artificial intelligence: the CONSORT-AI extension

X Liu, SC Rivera, D Moher, MJ Calvert… - The Lancet Digital …, 2020 - thelancet.com
The CONSORT 2010 statement provides minimum guidelines for reporting randomised
trials. Its widespread use has been instrumental in ensuring transparency in the evaluation …

[HTML][HTML] Guidelines for clinical trial protocols for interventions involving artificial intelligence: the SPIRIT-AI extension

SC Rivera, X Liu, AW Chan, AK Denniston… - The Lancet Digital …, 2020 - thelancet.com
The SPIRIT 2013 statement aims to improve the completeness of clinical trial protocol
reporting by providing evidence-based recommendations for the minimum set of items to be …

Hidden stratification causes clinically meaningful failures in machine learning for medical imaging

L Oakden-Rayner, J Dunnmon, G Carneiro… - Proceedings of the ACM …, 2020 - dl.acm.org
Machine learning models for medical image analysis often suffer from poor performance on
important subsets of a population that are not identified during training or testing. For …

[HTML][HTML] 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… - The Lancet Digital …, 2021 - thelancet.com
Background Chest x-rays are widely used in clinical practice; however, interpretation can be
hindered by human error and a lack of experienced thoracic radiologists. Deep learning has …

[HTML][HTML] 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… - Scientific reports, 2021 - nature.com
Artificial intelligence technology has advanced rapidly in recent years and has the potential
to improve healthcare outcomes. However, technology uptake will be largely driven by …

[HTML][HTML] Deep learning predicts hip fracture using confounding patient and healthcare variables

MA Badgeley, JR Zech, L Oakden-Rayner… - NPJ digital …, 2019 - nature.com
Hip fractures are a leading cause of death and disability among older adults. Hip fractures
are also the most commonly missed diagnosis on pelvic radiographs, and delayed …

Exploring large-scale public medical image datasets

L Oakden-Rayner - Academic radiology, 2020 - Elsevier
Rationale and Objectives Medical artificial intelligence systems are dependent on well
characterized large-scale datasets. Recently released public datasets have been of great …

[HTML][HTML] Precision radiology: predicting longevity using feature engineering and deep learning methods in a radiomics framework

L Oakden-Rayner, G Carneiro, T Bessen… - Scientific reports, 2017 - nature.com
Precision medicine approaches rely on obtaining precise knowledge of the true state of
health of an individual patient, which results from a combination of their genetic risks and …

Reading race: AI recognises patient's racial identity in medical images

I Banerjee, AR Bhimireddy, JL Burns, LA Celi… - arXiv preprint arXiv …, 2021 - arxiv.org
Background: In medical imaging, prior studies have demonstrated disparate AI performance
by race, yet there is no known correlation for race on medical imaging that would be obvious …