Get started
Author

Lauren Oakden‐Rayner

Observed names: L. Oakden-Rayner, Lauren Oakden-Rayner, Oakden-Rayner L, Oakden-Rayner, Lauren
Observed institutions: Royal Adelaide Hospital, University College Cork, Australian Institute of Business, Adelaide University, University College London, The University of Adelaide
ORCID: 0000-0001-5471-5202
Observed ORCID: 0000-0001-5471-5202
OpenAlex ID: A5068274591
TRIPOD+AI statement: updated guidance for reporting clinical prediction models that use regression or machine learning methods
2024 · Gary S Collins, Karel G M Moons, et al. · BMJ · 4,711
PDF
PROBAST+AI: an updated quality, risk of bias, and applicability assessment tool for prediction models using regression or artificial intelligence methods
2025 · Karel G M Moons, Johanna A A Damen, et al. · BMJ · 1,067
Reporting guideline for the early-stage clinical evaluation of decision support systems driven by artificial intelligence: DECIDE-AI
2022 · Baptiste Vasey, Myura Nagendran, et al. · Nature Medicine · 1,042
AI recognition of patient race in medical imaging: a modelling study
2022 · Judy Wawira Gichoya, Imon Banerjee, et al. · The Lancet Digital Health · 611
PDF
The value of standards for health datasets in artificial intelligence-based applications
2023 · Anmol Arora, Joseph E. Alderman, et al. · Nature Medicine · 302
PDF
The medical algorithmic audit
2022 · Xiaoxuan Liu, Ben Glocker, et al. · The Lancet Digital Health · 242
PDF
Tackling algorithmic bias and promoting transparency in health datasets: the STANDING Together consensus recommendations
2024 · Joseph E Alderman, Joanne Palmer, et al. · The Lancet Digital Health · 181
Machine learning and deep learning predictive models for long-term prognosis in patients with chronic obstructive pulmonary disease: a systematic review and meta-analysis
2023 · Luke A Smith, Lauren Oakden-Rayner, et al. · The Lancet Digital Health · 116
PDF
Developing, purchasing, implementing and monitoring AI tools in radiology: practical considerations. A multi-society statement from the ACR, CAR, ESR, RANZCR & RSNA
2024 · Adrian P. Brady, Bibb Allen, et al. · Insights into Imaging · 95
PDF
Validation and algorithmic audit of a deep learning system for the detection of proximal femoral fractures in patients in the emergency department: a diagnostic accuracy study
2022 · Lauren Oakden-Rayner, William Gale, et al. · The Lancet Digital Health · 93
PDF
Tackling bias in AI health datasets through the STANDING Together initiative
2022 · Shaswath Ganapathi, Jo Palmer, et al. · Nature Medicine · 82
Developing, Purchasing, Implementing and Monitoring AI Tools in Radiology: Practical Considerations. A Multi-Society Statement from the ACR, CAR, ESR, RANZCR and RSNA
2024 · Adrian P. Brady, Bibb Allen, et al. · Radiology Artificial Intelligence · 76
PDF
Medical artificial intelligence for clinicians: the lost cognitive perspective
2024 · Lana Tikhomirov, Carolyn Semmler, et al. · The Lancet Digital Health · 65
Concordance of randomised controlled trials for artificial intelligence interventions with the CONSORT-AI reporting guidelines
2024 · Alexander P. L. Martindale, Carrie D. Llewellyn, et al. · Nature Communications · 58
PDF
Developing, Purchasing, Implementing and Monitoring AI Tools in Radiology: Practical Considerations. A Multi-Society Statement From the ACR, CAR, ESR, RANZCR & RSNA
2024 · Adrian P. Brady, Bibb Allen, et al. · Canadian Association of Radiologists Journal · 54
Artificial intelligence and the future of radiographic scoring in rheumatoid arthritis: a viewpoint
2022 · Alix Bird, Lauren Oakden-Rayner, et al. · Arthritis Research & Therapy · 47
PDF
Developing, purchasing, implementing and monitoring AI tools in radiology: Practical considerations. A multi‐society statement from the ACR, CAR, ESR, RANZCR & RSNA
2024 · Adrian P Brady, Bibb Allen, et al. · Journal of Medical Imaging and Radiation Oncology · 44
What's fair is… fair? Presenting JustEFAB, an ethical framework for operationalizing medical ethics and social justice in the integration of clinical machine learning
2023 · Melissa Mccradden, Oluwadara Odusi, et al. · ACM Conference on Fairness, Accountability, and Transparency (FAccT) · 33
PDF
Improving the Fairness of Chest X-ray Classifiers
2022 · Zhang, Haoran, Dullerud, Natalie, et al. · arXiv (Cornell University) · 33
PDF
Reply to ‘Man against machine: diagnostic performance of a deep learning convolutional neural network for dermoscopic melanoma recognition in comparison to 58 dermatologists’ by Haenssle et al.
2018 · L. Oakden-Rayner · Annals of Oncology · 28
CANAIRI: the Collaboration for Translational Artificial Intelligence Trials in healthcare
2025 · Melissa D. McCradden, Alex John London, et al. · Nature Medicine · 27
Developing, Purchasing, Implementing and Monitoring AI Tools in Radiology: Practical Considerations. A Multi-Society Statement From the ACR, CAR, ESR, RANZCR & RSNA
2024 · Adrian P. Brady, Bibb Allen, et al. · Journal of the American College of Radiology · 24
PDF
Effects of a comprehensive brain computed tomography deep learning model on radiologist detection accuracy
2023 · Quinlan D. Buchlak, Cyril H. M. Tang, et al. · European Radiology · 24
PDF
Pre-thrombectomy prognostic prediction of large-vessel ischemic stroke using machine learning: A systematic review and meta-analysis
2022 · Minyan Zeng, Lauren Oakden-Rayner, et al. · Frontiers in Neurology · 17
PDF
Publisher Correction: Reporting guideline for the early-stage clinical evaluation of decision support systems driven by artificial intelligence: DECIDE-AI
2022 · Baptiste Vasey, Myura Nagendran, et al. · Nature Medicine · 16
PDF
Works count: 47
Citation count: 9,185
H-index: 23
I10-index: 28
Year
Topic
Type