Luke Oakden‐Rayner

4.9k total citations · 1 hit paper
18 papers, 1.4k citations indexed

About

Luke Oakden‐Rayner is a scholar working on Radiology, Nuclear Medicine and Imaging, Health Informatics and Epidemiology. According to data from OpenAlex, Luke Oakden‐Rayner has authored 18 papers receiving a total of 1.4k indexed citations (citations by other indexed papers that have themselves been cited), including 9 papers in Radiology, Nuclear Medicine and Imaging, 6 papers in Health Informatics and 5 papers in Epidemiology. Recurrent topics in Luke Oakden‐Rayner's work include Radiomics and Machine Learning in Medical Imaging (7 papers), Artificial Intelligence in Healthcare and Education (6 papers) and Radiology practices and education (5 papers). Luke Oakden‐Rayner is often cited by papers focused on Radiomics and Machine Learning in Medical Imaging (7 papers), Artificial Intelligence in Healthcare and Education (6 papers) and Radiology practices and education (5 papers). Luke Oakden‐Rayner collaborates with scholars based in Australia, United States and United Kingdom. Luke Oakden‐Rayner's co-authors include Andrew L. Beam, Marzyeh Ghassemi, Lyle J. Palmer, Stephen Bacchi, Jim Jannes, Timothy Kleinig, Andrew P. Bradley, Gustavo Carneiro, Sandy Patel and Taryn Bessen and has published in prestigious journals such as Stroke, Scientific Reports and BMJ Open.

In The Last Decade

Luke Oakden‐Rayner

18 papers receiving 1.4k citations

Hit Papers

align trajectories

What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

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

2021 681 citations Marzyeh Ghassemi, Luke Oakden‐Rayner et al. The Lancet Digital Health profile →

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name	h						Papers	Cites
Luke Oakden‐Rayner Australia	13	551	528	490	173	165	18	1.4k
Marcus A. Badgeley United States	16	431 0.8×	613 1.2×	708 1.4×	223 1.3×	197 1.2×	21	2.0k
John R. Zech United States	12	504 0.9×	618 1.2×	722 1.5×	167 1.0×	166 1.0×	29	1.6k
Emma Chen United States	7	593 1.1×	456 0.9×	414 0.8×	83 0.5×	88 0.5×	13	1.4k
Christoph Kern Germany	14	494 0.9×	449 0.9×	983 2.0×	177 1.0×	120 0.7×	36	1.8k
Gabriella Moraes United Kingdom	12	510 0.9×	481 0.9×	883 1.8×	101 0.6×	117 0.7×	22	1.6k
Mustafa Suleyman United Kingdom	6	672 1.2×	511 1.0×	463 0.9×	122 0.7×	74 0.4×	6	1.4k
Keno K. Bressem Germany	26	534 1.0×	427 0.8×	785 1.6×	136 0.8×	224 1.4×	109	1.8k
Constanza L. Andaur Navarro Netherlands	12	436 0.8×	367 0.7×	306 0.6×	136 0.8×	73 0.4×	18	1.1k
Oishi Banerjee United States	5	912 1.7×	773 1.5×	687 1.4×	104 0.6×	126 0.8×	7	2.2k
Jiming Xu China	5	674 1.2×	418 0.8×	416 0.8×	69 0.4×	83 0.5×	13	1.3k

Countries citing papers authored by Luke Oakden‐Rayner

Since Specialization

Citations

This map shows the geographic impact of Luke Oakden‐Rayner's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Luke Oakden‐Rayner with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Luke Oakden‐Rayner more than expected).

Fields of papers citing papers by Luke Oakden‐Rayner

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Luke Oakden‐Rayner. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Luke Oakden‐Rayner. The network helps show where Luke Oakden‐Rayner may publish in the future.

Co-authorship network of co-authors of Luke Oakden‐Rayner

This figure shows the co-authorship network connecting the top 25 collaborators of Luke Oakden‐Rayner. A scholar is included among the top collaborators of Luke Oakden‐Rayner based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Luke Oakden‐Rayner. Luke Oakden‐Rayner is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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18 of 18 papers shown

Bacchi, Stephen, Luke Oakden‐Rayner, David Menon, et al.. (2022). Prospective and external validation of stroke discharge planning machine learning models. Journal of Clinical Neuroscience. 96. 80–84. 8 indexed citations

Jones, Catherine M, Michael Milne, Cyril Tang, et al.. (2021). Assessment of the effect of a comprehensive chest radiograph deep learning model on radiologist reports and patient outcomes: a real-world observational study. BMJ Open. 11(12). e052902–e052902. 28 indexed citations

Ghassemi, Marzyeh, Luke Oakden‐Rayner, & Andrew L. Beam. (2021). The false hope of current approaches to explainable artificial intelligence in health care. The Lancet Digital Health. 3(11). e745–e750. 681 indexed citations breakdown →

Scheetz, Jane, Philip Rothschild, Myra B. McGuinness, et al.. (2021). A survey of clinicians on the use of artificial intelligence in ophthalmology, dermatology, radiology and radiation oncology. Scientific Reports. 11(1). 5193–5193. 162 indexed citations

Seah, Jarrel, Cyril Tang, Quinlan D. Buchlak, et al.. (2021). Effect of a comprehensive deep-learning model on the accuracy of chest x-ray interpretation by radiologists: a retrospective, multireader multicase study. The Lancet Digital Health. 3(8). e496–e506. 128 indexed citations

Franke, Kyle B., David Wang, Yiran Tan, et al.. (2021). Medical student knowledge and critical appraisal of machine learning: a multicentre international cross‐sectional study. Internal Medicine Journal. 51(9). 1539–1542. 12 indexed citations

Jones, Catherine M, Quinlan D. Buchlak, Luke Oakden‐Rayner, et al.. (2021). Chest radiographs and machine learning – Past, present and future. Journal of Medical Imaging and Radiation Oncology. 65(5). 538–544. 19 indexed citations

Nottage, Michelle, et al.. (2021). Assessing the accuracy of ⁶⁸Ga‐PSMA PET/CT compared with MRI in the initial diagnosis of prostate malignancy: A cohort analysis of 114 consecutive patients. Journal of Medical Imaging and Radiation Oncology. 66(3). 319–323. 3 indexed citations

Seah, Jarrel, Cyril Tang, Quinlan D. Buchlak, et al.. (2021). Do comprehensive deep learning algorithms suffer from hidden stratification? A retrospective study on pneumothorax detection in chest radiography. BMJ Open. 11(12). e053024–e053024. 9 indexed citations

10.

Bacchi, Stephen, Yiran Tan, Luke Oakden‐Rayner, et al.. (2020). Machine learning in the prediction of medical inpatient length of stay. Internal Medicine Journal. 52(2). 176–185. 38 indexed citations

11.

Bacchi, Stephen, Luke Oakden‐Rayner, David Menon, et al.. (2020). Stroke prognostication for discharge planning with machine learning: A derivation study. Journal of Clinical Neuroscience. 79. 100–103. 24 indexed citations

12.

Harvey, Hugh & Luke Oakden‐Rayner. (2020). Guidance for Interventional Trials Involving Artificial Intelligence. Radiology Artificial Intelligence. 2(6). e200228–e200228. 5 indexed citations

13.

Bacchi, Stephen, et al.. (2019). Deep learning in the detection of high-grade glioma recurrence using multiple MRI sequences: A pilot study. Journal of Clinical Neuroscience. 70. 11–13. 32 indexed citations

14.

Bacchi, Stephen, et al.. (2019). Deep Learning in the Prediction of Ischaemic Stroke Thrombolysis Functional Outcomes. Academic Radiology. 27(2). e19–e23. 74 indexed citations

15.

Bacchi, Stephen, et al.. (2019). Deep Learning Natural Language Processing Successfully Predicts the Cerebrovascular Cause of Transient Ischemic Attack-Like Presentations. Stroke. 50(3). 758–760. 41 indexed citations

16.

Gale, William A., Luke Oakden‐Rayner, Gustavo Carneiro, Lyle J. Palmer, & Andrew P. Bradley. (2019). Producing Radiologist-Quality Reports for Interpretable Deep Learning. Adelaide Research & Scholarship (AR&S) (University of Adelaide). 1275–1279. 32 indexed citations

17.

Finlayson, Samuel G., Hyunkwang Lee, Isaac S. Kohane, & Luke Oakden‐Rayner. (2018). Towards generative adversarial networks as a new paradigm for radiology education. arXiv (Cornell University). 1 indexed citations

18.

Oakden‐Rayner, Luke, Gustavo Carneiro, Taryn Bessen, et al.. (2017). Precision Radiology: Predicting longevity using feature engineering and deep learning methods in a radiomics framework. Scientific Reports. 7(1). 1648–1648. 102 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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