| 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 | 960 | 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 | 893 | 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 | 774 | 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 | 424 | 2022 |
| 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 | 423 | 2020 |
| 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 | 408 | 2022 |
| TRIPOD+ AI statement: updated guidance for reporting clinical prediction models that use regression or machine learning methods GS Collins, KGM Moons, P Dhiman, RD Riley, AL Beam, B Van Calster, ... bmj 385, 2024 | 325 | 2024 |
| 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 | 236 | 2019 |
| Exploring large-scale public medical image datasets L Oakden-Rayner Academic radiology 27 (1), 106-112, 2020 | 199 | 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), 1648, 2017 | 185 | 2017 |
| 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 | 184 | 2021 |
| The medical algorithmic audit X Liu, B Glocker, MM McCradden, M Ghassemi, AK Denniston, ... The Lancet Digital Health 4 (5), e384-e397, 2022 | 181 | 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 | 177 | 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 | 134 | 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 | 118 | 2020 |
| The value of standards for health datasets in artificial intelligence-based applications A Arora, JE Alderman, J Palmer, S Ganapathi, E Laws, MD Mccradden, ... Nature Medicine 29 (11), 2929-2938, 2023 | 108 | 2023 |
| 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 | 102* | 2019 |
| Developing, purchasing, implementing and monitoring AI tools in radiology: practical considerations. A multi-society statement from the ACR, CAR, ESR, RANZCR & RSNA AP Brady, B Allen, J Chong, E Kotter, N Kottler, J Mongan, ... Canadian Association of Radiologists Journal 75 (2), 226-244, 2024 | 94 | 2024 |
| 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 | 87 | 2022 |
| 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 | 82 | 2021 |
Lyle John PalmerProfessor of Genetic Epidemiology, Adelaide UniversityVerified email at adelaide.edu.au
