James A. White

8.6k total citations · 2 hit papers
181 papers, 4.8k citations indexed

About

James A. White is a scholar working on Cardiology and Cardiovascular Medicine, Radiology, Nuclear Medicine and Imaging and Surgery. According to data from OpenAlex, James A. White has authored 181 papers receiving a total of 4.8k indexed citations (citations by other indexed papers that have themselves been cited), including 127 papers in Cardiology and Cardiovascular Medicine, 79 papers in Radiology, Nuclear Medicine and Imaging and 27 papers in Surgery. Recurrent topics in James A. White's work include Cardiac Imaging and Diagnostics (66 papers), Cardiovascular Function and Risk Factors (60 papers) and Advanced MRI Techniques and Applications (44 papers). James A. White is often cited by papers focused on Cardiac Imaging and Diagnostics (66 papers), Cardiovascular Function and Risk Factors (60 papers) and Advanced MRI Techniques and Applications (44 papers). James A. White collaborates with scholars based in Canada, United States and United Kingdom. James A. White's co-authors include D. Ian Paterson, Matthias G. Friedrich, Jeanette Schulz‐Menger, Hassan Abdel‐Aty, Peter Liu, Godtfred Holmvang, Leslie T. Cooper, Pauline Alakija, Sanjay Prasad and Andreas Kumar and has published in prestigious journals such as Circulation, SHILAP Revista de lepidopterología and Journal of the American College of Cardiology.

In The Last Decade

James A. White

170 papers receiving 4.7k 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.

Cardiovascular Magnetic Resonance in Myocarditis: A JACC White Paper

2009 1.6k citations James A. White, D. Ian Paterson et al. Journal of the American College of Cardiology profile →
Bias recognition and mitigation strategies in artificial intelligence healthcare applications

2025 54 citations Fereshteh Hasanzadeh, James A. White et al. npj Digital Medicine profile →

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
James A. White Canada	29	3.5k	1.6k	952	493	409	181	4.8k
Stefan L. Zimmerman United States	38	3.4k 1.0×	1.3k 0.8×	449 0.5×	281 0.6×	708 1.7×	183	4.4k
Jonathan Chan Australia	32	2.7k 0.8×	1.6k 1.0×	576 0.6×	353 0.7×	462 1.1×	129	3.5k
Michael D. Taylor United States	33	1.8k 0.5×	531 0.3×	1.2k 1.3×	596 1.2×	523 1.3×	168	3.8k
Francesco Santini Italy	29	1.1k 0.3×	701 0.4×	633 0.7×	466 0.9×	731 1.8×	169	2.7k
Steven Dymarkowski Belgium	44	1.5k 0.4×	1.5k 0.9×	1.5k 1.6×	519 1.1×	1.3k 3.2×	117	5.0k
Sanjeev G. Shroff United States	39	3.8k 1.1×	1.0k 0.6×	1.1k 1.2×	327 0.7×	587 1.4×	121	5.9k
David Liang United States	38	3.8k 1.1×	1.2k 0.7×	1.8k 1.9×	1.1k 2.2×	1.2k 3.0×	171	5.3k
Robert Manka Switzerland	35	1.9k 0.6×	2.6k 1.6×	656 0.7×	289 0.6×	340 0.8×	204	4.0k
Sanjay Prasad United Kingdom	42	8.9k 2.6×	3.9k 2.4×	1.8k 1.9×	1.0k 2.1×	844 2.1×	222	11.0k
Minjie Lu China	31	1.7k 0.5×	1.0k 0.6×	713 0.7×	283 0.6×	229 0.6×	208	3.1k

Countries citing papers authored by James A. White

Since Specialization

Citations

This map shows the geographic impact of James A. White'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 James A. White with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites James A. White more than expected).

Fields of papers citing papers by James A. White

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by James A. White. 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 James A. White. The network helps show where James A. White may publish in the future.

Co-authorship network of co-authors of James A. White

This figure shows the co-authorship network connecting the top 25 collaborators of James A. White. A scholar is included among the top collaborators of James A. White 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 James A. White. James A. White is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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20 of 20 papers shown

Greenberg, Matthew, et al.. (2025). Personalized decision making for coronary artery disease treatment using offline reinforcement learning. npj Digital Medicine. 8(1). 99–99. 1 indexed citations

Dykstra, Steven, Rhys I. Beaudry, Yuanchao Feng, et al.. (2025). An institutional framework to support ethical fair and equitable artificial intelligence augmented care. npj Digital Medicine. 8(1). 84–84. 2 indexed citations

Tse, Justin J., Fereshteh Hasanzadeh, Steven Dykstra, et al.. (2025). Phenotypic clustering of hypertrophic cardiomyopathy using 3D cardiac shape models and associations with future adverse outcomes. Journal of Cardiovascular Magnetic Resonance. 27. 101329–101329. 1 indexed citations

Tse, Justin J., Steven Dykstra, Fereshteh Hasanzadeh, et al.. (2025). Derivation of a risk score for prediction of future adverse outcomes in asymptomatic hypertrophic cardiomyopathy patients from the CIROC registry. Journal of Cardiovascular Magnetic Resonance. 27. 101312–101312. 1 indexed citations

Hasanzadeh, Fereshteh, Theresa M. Beckie, Cory White, et al.. (2025). 3D quantification of maximal left ventricular wall thickness: associations with global fibrosis burden and future adverse outcomes in hypertrophic cardiomyopathy. Journal of Cardiovascular Magnetic Resonance. 27. 101309–101309. 1 indexed citations

Fine, Nowell M., Sunil V. Kalmady, Russell Greiner, et al.. (2024). Machine Learning For Risk Prediction After Heart Failure Emergency Department Visit or Hospital Admission Using Administrative Health Data. SHILAP Revista de lepidopterología. 3(10). e0000636–e0000636. 1 indexed citations

Dykstra, Steven, Patrick Feng, Jonathan G. Howlett, et al.. (2024). Multiparametric Response To Disease-Modifying Therapy For Transthyretin Amyloidosis Cardiomyopathy. Journal of Cardiac Failure. 30(1). 165–166. 2 indexed citations

Fedak, Paul W.M., et al.. (2023). Oscillatory shear stress is elevated in patients with bicuspid aortic valve and aortic regurgitation: a 4D flow cardiovascular magnetic resonance cross-sectional study. European Heart Journal - Cardiovascular Imaging. 25(3). 404–412. 4 indexed citations

Vandenberk, Bert, Steven Dykstra, Derek S. Chew, et al.. (2023). Patients with non-ischemic cardiomyopathy and mid-wall striae have similar arrhythmic outcomes as ischemic cardiomyopathy. The International Journal of Cardiovascular Imaging. 39(10). 2005–2014. 1 indexed citations

10.

Guzzardi, David G., James A. White, Steven Dykstra, et al.. (2022). Normative healthy reference values for global and segmental 3D principal and geometry dependent strain from cine cardiac magnetic resonance imaging. The International Journal of Cardiovascular Imaging. 39(1). 115–134. 1 indexed citations

11.

Kim, Han‐Suk, et al.. (2021). Left Ventricular Flow Distribution as a Novel Flow Biomarker in Atrial Fibrillation. Frontiers in Bioengineering and Biotechnology. 9. 725121–725121. 6 indexed citations

12.

Pagano, Joseph J., Justin A. Ezekowitz, Gavin Y. Oudit, et al.. (2020). Layer-specific strain in patients with heart failure using cardiovascular magnetic resonance: not all layers are the same. Journal of Cardiovascular Magnetic Resonance. 22(1). 81–81. 28 indexed citations

13.

Svystonyuk, Daniyil A., H.E. Mewhort, Ali Fatehi Hassanabad, et al.. (2020). Acellular bioscaffolds redirect cardiac fibroblasts and promote functional tissue repair in rodents and humans with myocardial injury. Scientific Reports. 10(1). 9459–9459. 26 indexed citations

14.

Miller, Robert J.H., Danielle A. Southern, Mei Zhang, et al.. (2017). LV Mass Independently Predicts Mortality and Need for Future Revascularization in Patients Undergoing Diagnostic Coronary Angiography. JACC. Cardiovascular imaging. 11(3). 423–433. 28 indexed citations

15.

White, James A. & Nowell M. Fine. (2016). Recent Advances in Cardiovascular Imaging Relevant to the Management of Patients with Suspected Cardiac Amyloidosis. Current Cardiology Reports. 18(8). 77–77. 15 indexed citations

16.

Marco, Andrea Di, Ignasi Anguera, Matthias Schmitt, et al.. (2016). Late Gadolinium Enhancement and the Risk for Ventricular Arrhythmias or Sudden Death in Dilated Cardiomyopathy. JACC Heart Failure. 5(1). 28–38. 252 indexed citations

17.

Nevis, Immaculate, et al.. (2013). MID-WALL STRIAE PATTERN ON LATE GADOLINIUM ENHANCEMENT IMAGING PREDICTS FUTURE CARDIOVASCULAR EVENTS IN PATIENTS WITH SYSTOLIC DYSFUNCTION. Journal of the American College of Cardiology. 61(10). E830–E830. 1 indexed citations

18.

Yuan, Jing, et al.. (2012). A Convex Relaxation Approach to Fat/Water Separation with Minimum Label Description. Lecture notes in computer science. 15(Pt 2). 519–526. 4 indexed citations

19.

Khan, Razi, et al.. (2010). Cardiac Sarcoid in a Heart Transplant Recipient: Detection With Cardiac Magnetic Resonance Imaging. Transplantation Proceedings. 42(5). 1976–1978. 12 indexed citations

20.

Gissane, Conor, et al.. (1997). Differences in the incidence of injury between rugby league forwards and backs.. PubMed. 29(4). 91–4. 39 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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