User profiles for "author:Luke Oakden-Rayner"
Lauren Oakden-RaynerAustralian 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 …
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
The CONSORT 2010 statement provides minimum guidelines for reporting randomised
trials. Its widespread use has been instrumental in ensuring transparency in the evaluation …
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
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 …
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
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 …
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
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 …
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
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 …
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 …
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 …
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 …
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
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 …
by race, yet there is no known correlation for race on medical imaging that would be obvious …