Michal Cohen‐Shelly

608 total citations · 1 hit paper
14 papers, 354 citations indexed

About

Michal Cohen‐Shelly is a scholar working on Cardiology and Cardiovascular Medicine, Molecular Biology and Surgery. According to data from OpenAlex, Michal Cohen‐Shelly has authored 14 papers receiving a total of 354 indexed citations (citations by other indexed papers that have themselves been cited), including 11 papers in Cardiology and Cardiovascular Medicine, 3 papers in Molecular Biology and 2 papers in Surgery. Recurrent topics in Michal Cohen‐Shelly's work include Cardiovascular Function and Risk Factors (4 papers), ECG Monitoring and Analysis (3 papers) and Cardiac pacing and defibrillation studies (3 papers). Michal Cohen‐Shelly is often cited by papers focused on Cardiovascular Function and Risk Factors (4 papers), ECG Monitoring and Analysis (3 papers) and Cardiac pacing and defibrillation studies (3 papers). Michal Cohen‐Shelly collaborates with scholars based in United States, Israel and Japan. Michal Cohen‐Shelly's co-authors include Paul A. Friedman, Zachi I. Attia, Francisco López-Jiménez, Peter A. Noseworthy, Dennis H. Murphree, Suraj Kapa, Patrick W. Johnson, Rickey E. Carter, Saki Ito and Héctor I. Michelena and has published in prestigious journals such as SHILAP Revista de lepidopterología, European Heart Journal and The American Journal of Cardiology.

In The Last Decade

Michal Cohen‐Shelly

13 papers receiving 344 citations

Hit Papers

Electrocardiogram screening for aortic valve stenosis usi... 2021 2026 2022 2024 2021 40 80 120

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Michal Cohen‐Shelly United States 7 267 76 52 50 37 14 354
Irena Andršová Czechia 11 213 0.8× 54 0.7× 40 0.8× 32 0.6× 13 0.4× 27 305
Zeineb Bouzid United States 6 255 1.0× 78 1.0× 52 1.0× 36 0.7× 22 0.6× 14 368
Ilke Ӧzcan United States 9 164 0.6× 110 1.4× 18 0.3× 38 0.8× 28 0.8× 31 280
Grant Duffy United States 8 189 0.7× 156 2.1× 73 1.4× 32 0.6× 28 0.8× 14 331
Lauri Holmström Finland 11 341 1.3× 53 0.7× 13 0.3× 18 0.4× 15 0.4× 48 451
Belinda Sandler United Kingdom 13 267 1.0× 27 0.4× 30 0.6× 34 0.7× 41 1.1× 35 379
Cheuk To Chung China 10 109 0.4× 21 0.3× 17 0.3× 31 0.6× 20 0.5× 24 240
Kristian Hellenkamp Germany 11 324 1.2× 86 1.1× 5 0.1× 102 2.0× 64 1.7× 24 469
Min Tang China 12 326 1.2× 112 1.5× 5 0.1× 47 0.9× 59 1.6× 45 482
Isabel G. Scalia United States 8 168 0.6× 101 1.3× 15 0.3× 61 1.2× 30 0.8× 35 239

Countries citing papers authored by Michal Cohen‐Shelly

Since Specialization
Citations

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

Fields of papers citing papers by Michal Cohen‐Shelly

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Michal Cohen‐Shelly. 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 Michal Cohen‐Shelly. The network helps show where Michal Cohen‐Shelly may publish in the future.

Co-authorship network of co-authors of Michal Cohen‐Shelly

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

All Works

14 of 14 papers shown
1.
Barbash, Israel M., et al.. (2025). Estimating LVEF from ECG with GPT-4o Fine-Tuned Vision: A Novel Approach in AI-Driven Cardiac Diagnostics. Journal of Medical Systems. 49(1). 157–157.
2.
Cohen‐Shelly, Michal, Nimrod Blank, Dor Lotan, et al.. (2024). Artificial Intelligence Assessment of Biological Age From Transthoracic Echocardiography: Discrepancies with Chronologic Age Predict Significant Excess Mortality. Journal of the American Society of Echocardiography. 37(8). 725–735. 5 indexed citations
3.
Cohen‐Shelly, Michal, Noam Barda, Eliahu D. Aflalo, et al.. (2024). Artificial intelligence derived electrocardiograms: a comparative study of network architectures predicting sex and left ventricular dysfunction. European Heart Journal. 45(Supplement_1). 1 indexed citations
4.
Ito, Saki, Michal Cohen‐Shelly, Zachi I. Attia, et al.. (2023). Correlation between artificial intelligence-enabled electrocardiogram and echocardiographic features in aortic stenosis. European Heart Journal - Digital Health. 4(3). 196–206. 7 indexed citations
5.
Shelly, Shahar, Francisco Lopez‐Jimenez, Michal Cohen‐Shelly, et al.. (2023). Accelerated Aging in LMNA Mutations Detected by Artificial Intelligence ECG–Derived Age. Mayo Clinic Proceedings. 98(4). 522–532. 8 indexed citations
6.
Klein, Christopher J., Ilke Ӧzcan, Zachi I. Attia, et al.. (2022). Electrocardiogram-Artificial Intelligence and Immune-Mediated Necrotizing Myopathy: Predicting Left Ventricular Dysfunction and Clinical Outcomes. SHILAP Revista de lepidopterología. 6(5). 450–457. 6 indexed citations
7.
Ӧzcan, Ilke, Takumi Toya, Michal Cohen‐Shelly, et al.. (2022). Artificial intelligence–derived cardiac ageing is associated with cardiac events post-heart transplantation. European Heart Journal - Digital Health. 3(4). 516–524. 2 indexed citations
8.
Adedinsewo, Demilade, Patrick W. Johnson, Erika J. Douglass, et al.. (2022). Digitizing paper based ECG files to foster deep learning based analysis of existing clinical datasets: An exploratory analysis. Intelligence-Based Medicine. 6. 100070–100070. 5 indexed citations
9.
Cohen‐Shelly, Michal, Zachi I. Attia, Paul A. Friedman, et al.. (2021). Electrocardiogram screening for aortic valve stenosis using artificial intelligence. European Heart Journal. 42(30). 2885–2896. 126 indexed citations breakdown →
10.
Grogan, Martha, Francisco López-Jiménez, Michal Cohen‐Shelly, et al.. (2021). Artificial Intelligence–Enhanced Electrocardiogram for the Early Detection of Cardiac Amyloidosis. Mayo Clinic Proceedings. 96(11). 2768–2778. 76 indexed citations
11.
Siontis, Konstantinos C., Kan Liu, J. Martijn Bos, et al.. (2021). Detection of hypertrophic cardiomyopathy by an artificial intelligence electrocardiogram in children and adolescents. International Journal of Cardiology. 340. 42–47. 49 indexed citations
12.
Shrivastava, Sanskriti, Michal Cohen‐Shelly, Zachi I. Attia, et al.. (2021). Artificial Intelligence-Enabled Electrocardiography to Screen Patients with Dilated Cardiomyopathy. The American Journal of Cardiology. 155. 121–127. 28 indexed citations
13.
Toya, Takumi, Ali Ahmad, Zachi I. Attia, et al.. (2021). Vascular Aging Detected by Peripheral Endothelial Dysfunction Is Associated With ECG‐Derived Physiological Aging. Journal of the American Heart Association. 10(3). e018656–e018656. 39 indexed citations
14.
Ӧzcan, Ilke, Takumi Toya, Michal Cohen‐Shelly, et al.. (2021). Artificial intelligence derived age algorithm after heart transplantation. European Heart Journal. 42(Supplement_1). 2 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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