Eli Konen

7.5k total citations
182 papers, 4.8k citations indexed

About

Eli Konen is a scholar working on Radiology, Nuclear Medicine and Imaging, Cardiology and Cardiovascular Medicine and Surgery. According to data from OpenAlex, Eli Konen has authored 182 papers receiving a total of 4.8k indexed citations (citations by other indexed papers that have themselves been cited), including 86 papers in Radiology, Nuclear Medicine and Imaging, 45 papers in Cardiology and Cardiovascular Medicine and 42 papers in Surgery. Recurrent topics in Eli Konen's work include Cardiac Imaging and Diagnostics (26 papers), Radiomics and Machine Learning in Medical Imaging (24 papers) and AI in cancer detection (16 papers). Eli Konen is often cited by papers focused on Cardiac Imaging and Diagnostics (26 papers), Radiomics and Machine Learning in Medical Imaging (24 papers) and AI in cancer detection (16 papers). Eli Konen collaborates with scholars based in Israel, United States and Canada. Eli Konen's co-authors include Eyal Klang, Yiftach Barash, Orly Goitein, Vera Sorin, Hayit Greenspan, Elio Di Segni, Victor Guetta, Idit Diamant, Sivan Lieberman and Yaniv Bar and has published in prestigious journals such as Journal of Clinical Oncology, Blood and Journal of the American College of Cardiology.

In The Last Decade

Eli Konen

175 papers receiving 4.7k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Eli Konen Israel 40 1.6k 1.2k 1.2k 1.0k 820 182 4.8k
Bettina Baeßler Germany 30 3.0k 1.9× 478 0.4× 830 0.7× 735 0.7× 314 0.4× 100 4.3k
Hendrik von Tengg‐Kobligk Germany 37 1.5k 0.9× 1.4k 1.2× 1.3k 1.1× 2.2k 2.1× 296 0.4× 187 5.4k
Kyunghwa Han South Korea 42 3.9k 2.4× 1.7k 1.4× 387 0.3× 1.7k 1.7× 1.2k 1.5× 328 8.0k
David Snead United Kingdom 39 2.3k 1.4× 1.2k 1.0× 906 0.8× 663 0.6× 382 0.5× 128 6.6k
Joon Beom Seo South Korea 46 3.7k 2.3× 1.0k 0.9× 625 0.5× 4.8k 4.6× 718 0.9× 285 8.7k
Eric K. Oermann United States 32 1.1k 0.6× 711 0.6× 164 0.1× 1.1k 1.0× 530 0.6× 122 4.6k
Zeynettin Akkus United States 21 1.5k 0.9× 255 0.2× 369 0.3× 580 0.6× 253 0.3× 54 3.2k
Dominik Fleischmann United States 48 4.0k 2.5× 2.0k 1.6× 1.3k 1.0× 3.1k 3.0× 585 0.7× 244 8.5k
David Liang United States 38 1.2k 0.7× 1.8k 1.5× 3.8k 3.1× 1.2k 1.2× 1.1k 1.3× 171 5.3k
Jonathan Weir‐McCall United Kingdom 22 1.6k 1.0× 629 0.5× 1.4k 1.2× 644 0.6× 632 0.8× 125 2.9k

Countries citing papers authored by Eli Konen

Since Specialization
Citations

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

Fields of papers citing papers by Eli Konen

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Eli Konen. 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 Eli Konen. The network helps show where Eli Konen may publish in the future.

Co-authorship network of co-authors of Eli Konen

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

All Works

20 of 20 papers shown
1.
Strange, Chad D., et al.. (2024). Thymic Imaging Pitfalls and Strategies for Optimized Diagnosis. Radiographics. 44(5). e230091–e230091. 4 indexed citations
2.
Sorin, Vera, Dana Brin, Yiftach Barash, et al.. (2024). Large Language Models and Empathy: Systematic Review. Journal of Medical Internet Research. 26. e52597–e52597. 48 indexed citations
3.
Brin, Dana, Vera Sorin, Eli Konen, et al.. (2024). How GPT models perform on the United States medical licensing examination: a systematic review. Discover Applied Sciences. 6(10). 3 indexed citations
4.
Brin, Dana, Vera Sorin, Yiftach Barash, et al.. (2024). Assessing GPT-4 multimodal performance in radiological image analysis. European Radiology. 35(4). 1959–1965. 22 indexed citations
5.
Anteby, Roi, et al.. (2023). Artificial intelligence for X-ray scaphoid fracture detection: a systematic review and diagnostic test accuracy meta-analysis. European Radiology. 34(7). 4341–4351. 13 indexed citations
6.
Fardman, Alexander, Avi Sabbag, Eli Konen, et al.. (2023). Myocardial edema measured by T2 mapping is an independent predictor of ventricular arrhythmias in hypertrophic cardiomyopathy. European Heart Journal. 44(Supplement_2). 1 indexed citations
7.
Twig, Gilad, et al.. (2023). The impact on clinical outcomes after 1 year of implementation of an artificial intelligence solution for the detection of intracranial hemorrhage. International Journal of Emergency Medicine. 16(1). 50–50. 11 indexed citations
8.
9.
Sorin, Vera, Dana Brin, Yiftach Barash, et al.. (2023). Large Language Models and Empathy: Systematic Review (Preprint). 2 indexed citations
10.
Brin, Dana, Vera Sorin, Eli Konen, et al.. (2023). How Large Language Models Perform on the United States Medical Licensing Examination: A Systematic Review. medRxiv. 5 indexed citations
11.
12.
Oren, Daniel, Orly Goitein, Yafim Brodov, et al.. (2022). Post–ST‐Segment–Elevation Myocardial Infarction Platelet Reactivity Is Associated With the Extent of Microvascular Obstruction and Infarct Size as Determined by Cardiac Magnetic Resonance Imaging. Journal of the American Heart Association. 11(3). e020973–e020973. 11 indexed citations
13.
Wolf, Michael S., et al.. (2021). Anisotropic neural deblurring for MRI acceleration. International Journal of Computer Assisted Radiology and Surgery. 17(2). 315–327. 3 indexed citations
14.
Tsarfaty, Galia, et al.. (2019). Neural Segmentation of Seeding ROIs (sROIs) for Pre-Surgical Brain Tractography. IEEE Transactions on Medical Imaging. 39(5). 1655–1667. 6 indexed citations
15.
Hamdan, Ashraf, Israel M. Barbash, Ehud Schwammenthal, et al.. (2017). Sex differences in aortic root and vascular anatomy in patients undergoing transcatheter aortic valve implantation: A computed-tomographic study. Journal of cardiovascular computed tomography. 11(2). 87–96. 19 indexed citations
16.
Goitein, Orly, Noam Fink, Ilan Hay, et al.. (2017). Cardiac CT Angiography (CCTA) predicts left atrial appendage occluder device size and procedure outcome. International journal of cardiac imaging. 33(5). 739–747. 26 indexed citations
17.
Hamdan, Ashraf, Ernst Wellnhofer, Eli Konen, et al.. (2014). Coronary CT angiography for the detection of coronary artery stenosis in patients referred for transcatheter aortic valve replacement. Journal of cardiovascular computed tomography. 9(1). 31–41. 50 indexed citations
18.
Konen, Eli, Tammar Kushnir, Frederick H. Epstein, et al.. (2013). Non-invasive assessment of experimental autoimmune myocarditis in rats using a 3 T clinical MRI scanner. European Heart Journal - Cardiovascular Imaging. 14(11). 1069–1079. 11 indexed citations
19.
Rubinshtein, Ronen, Arik Wolak, Orly Goitein, et al.. (2011). [Appropriateness criteria for the use of cardiac computed tomography: position paper of the Israeli Heart Society and the Israeli Society of Radiology].. PubMed. 150(10). 801–5, 813. 1 indexed citations
20.
Beigel, Roy, Orly Goitein, Pierre Chouraqui, et al.. (2010). Fast track evaluation of patients with acute chest pain: experience in a large-scale chest pain unit in Israel.. PubMed. 12(6). 329–33. 7 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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