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

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.

Electrocardiogram screening for aortic valve stenosis using artificial intelligence

2021 126 citations Michal Cohen‐Shelly, Zachi I. Attia et al. European Heart Journal profile →

Peers

Michal Cohen‐Shelly

CCM

RNMI

PRM

EPIDE

Irena Andršová Czechia

Zeineb Bouzid United States

Ilke Ӧzcan United States

Grant Duffy United States

Lauri Holmström Finland

Belinda Sandler United Kingdom

Cheuk To Chung China

Kristian Hellenkamp Germany

Min Tang China

Isabel G. Scalia United States

Irena Andršová Czechia

Michal Cohen‐Shelly

213 ×0.8

CCM

54 ×0.7

RNMI

40 ×0.8

32 ×0.6

PRM

13 ×0.4

EPIDE

Citations per year, relative to Michal Cohen‐Shelly Michal Cohen‐Shelly (= 1×) peers Irena Andršová

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

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14 of 14 papers shown

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.

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

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

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

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

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

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

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

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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