Ashley Beecy

1.3k total citations
20 papers, 211 citations indexed

About

Ashley Beecy is a scholar working on Cardiology and Cardiovascular Medicine, Health Informatics and Health Information Management. According to data from OpenAlex, Ashley Beecy has authored 20 papers receiving a total of 211 indexed citations (citations by other indexed papers that have themselves been cited), including 11 papers in Cardiology and Cardiovascular Medicine, 5 papers in Health Informatics and 3 papers in Health Information Management. Recurrent topics in Ashley Beecy's work include Cardiovascular Function and Risk Factors (6 papers), Artificial Intelligence in Healthcare and Education (5 papers) and Heart Failure Treatment and Management (4 papers). Ashley Beecy is often cited by papers focused on Cardiovascular Function and Risk Factors (6 papers), Artificial Intelligence in Healthcare and Education (5 papers) and Heart Failure Treatment and Management (4 papers). Ashley Beecy collaborates with scholars based in United States, United Kingdom and Singapore. Ashley Beecy's co-authors include Subhi J. Al’Aref, James K. Min, Maria Karas, Richard B. Devereux, Jiwon Kim, Jonathan W. Weinsaft, Amanda Su, Rocío Pérez-Johnston, Madeline R. Sterling and Evelyn M. Horn 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

Ashley Beecy

19 papers receiving 209 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Ashley Beecy United States 9 112 64 38 29 24 20 211
Thabo Mahendiran Switzerland 8 98 0.9× 115 1.8× 77 2.0× 20 0.7× 24 1.0× 35 222
Akshay Khunte United States 8 98 0.9× 34 0.5× 44 1.2× 9 0.3× 15 0.6× 19 187
Emma Behnken United States 11 188 1.7× 42 0.7× 66 1.7× 19 0.7× 58 2.4× 22 406
Timothy M. Loftus United States 5 195 1.7× 47 0.7× 36 0.9× 11 0.4× 14 0.6× 10 310
Sae Won Choi South Korea 10 35 0.3× 31 0.5× 20 0.5× 12 0.4× 35 1.5× 21 242
Jarrod Frizzell United States 4 144 1.3× 37 0.6× 34 0.9× 18 0.6× 67 2.8× 13 300
Pradnya Brijmohan Bhattad United States 7 48 0.4× 21 0.3× 33 0.9× 11 0.4× 30 1.3× 29 232
Haleh Hashemighouchani United States 7 69 0.6× 18 0.3× 48 1.3× 9 0.3× 21 0.9× 11 195
Sehj Kashyap United States 6 66 0.6× 46 0.7× 117 3.1× 21 0.7× 42 1.8× 10 311
Alana Lewis United States 8 171 1.5× 30 0.5× 11 0.3× 6 0.2× 34 1.4× 11 276

Countries citing papers authored by Ashley Beecy

Since Specialization
Citations

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

Fields of papers citing papers by Ashley Beecy

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Ashley Beecy

This figure shows the co-authorship network connecting the top 25 collaborators of Ashley Beecy. A scholar is included among the top collaborators of Ashley Beecy 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 Ashley Beecy. Ashley Beecy 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.
Rosenthal, Jacob, Ashley Beecy, & Mert R. Sabuncu. (2025). Rethinking clinical trials for medical AI with dynamic deployments of adaptive systems. npj Digital Medicine. 8(1). 252–252. 13 indexed citations
2.
3.
Turchioe, Meghan Reading, Natalie C. Benda, Alison Hermann, et al.. (2024). Preparing for the bedside—optimizing a postpartum depression risk prediction model for clinical implementation in a health system. Journal of the American Medical Informatics Association. 31(6). 1258–1267. 12 indexed citations
4.
Assen, Marly van, et al.. (2024). Implications of Bias in Artificial Intelligence: Considerations for Cardiovascular Imaging. Current Atherosclerosis Reports. 26(4). 91–102. 11 indexed citations
5.
Long, Ann C., Christopher M. Haggerty, Dustin N. Hartzel, et al.. (2024). DELINEATE-Regurgitation: deep learning for automated assessment of aortic, mitral, and tricuspid regurgitation from echocardiography. European Heart Journal. 45(Supplement_1).
6.
Wang, Fei & Ashley Beecy. (2024). Implementing AI models in clinical workflows: a roadmap. BMJ evidence-based medicine. 30(5). bmjebm–2023. 5 indexed citations
7.
Beecy, Ashley, Chris Longhurst, Karandeep Singh, Robert M. Wachter, & Sara G. Murray. (2024). The Chief Health AI Officer — An Emerging Role for an Emerging Technology. NEJM AI. 1(7). 7 indexed citations
8.
Zhang, Yiye, et al.. (2024). Implementation of a Machine Learning Risk Prediction Model for Postpartum Depression in the Electronic Health Records.. PubMed. 2024. 1057–1066. 4 indexed citations
9.
Sendak, Mark, Vincent X. Liu, Ashley Beecy, et al.. (2024). Strengthening the use of artificial intelligence within healthcare delivery organizations: balancing regulatory compliance and patient safety. Journal of the American Medical Informatics Association. 31(7). 1622–1627. 3 indexed citations
10.
Jing, Linyuan, Dustin N. Hartzel, Daniel Rocha, et al.. (2023). Abstract 14647: EchoNext: An ECG-Based Deep Learning Model to Detect Structural Heart Disease. Circulation. 148(Suppl_1). 2 indexed citations
11.
12.
Beecy, Ashley, Evan Sholle, Zhuoran Xu, et al.. (2020). Utilizing electronic health data and machine learning for the prediction of 30-day unplanned readmission or all-cause mortality in heart failure. SHILAP Revista de lepidopterología. 1(2). 71–79. 10 indexed citations
13.
14.
Beecy, Ashley, et al.. (2020). Development of novel machine learning model for right ventricular quantification on echocardiography—A multimodality validation study. Echocardiography. 37(5). 688–697. 14 indexed citations
15.
Kim, Jiwon, Ashley Beecy, Noel Codella, et al.. (2019). Machine learning derived segmentation of phase velocity encoded cardiovascular magnetic resonance for fully automated aortic flow quantification. Journal of Cardiovascular Magnetic Resonance. 21(1). 1–1. 62 indexed citations
16.
Beecy, Ashley, et al.. (2019). DEVELOPMENT OF A NOVEL DEEP LEARNING MODEL FOR RIGHT VENTRICULAR QUANTIFICATION ON ECHOCARDIOGRAPHY: A MULTIMODALITY VALIDATION STUDY. Journal of the American College of Cardiology. 73(9). 1437–1437. 1 indexed citations
17.
Su, Amanda, Subhi J. Al’Aref, Ashley Beecy, James K. Min, & Maria Karas. (2019). Clinical and Socioeconomic Predictors of Heart Failure Readmissions: A Review of Contemporary Literature. Mayo Clinic Proceedings. 94(7). 1304–1320. 32 indexed citations
18.
Sterling, Madeline R., et al.. (2018). Discharge Processes and 30-Day Readmission Rates of Patients Hospitalized for Heart Failure on General Medicine and Cardiology Services. The American Journal of Cardiology. 121(9). 1076–1080. 17 indexed citations
19.
Beecy, Ashley, Bríain ó Hartaigh, Joshua Schulman‐Marcus, et al.. (2018). Association between epicardial fat volume and fractional flow reserve: An analysis of the determination of fractional flow reserve (DeFACTO) study. Clinical Imaging. 51. 30–34. 2 indexed citations
20.
Goyal, Parag, Madeline R. Sterling, Ashley Beecy, et al.. (2016). Patterns of scheduled follow-up appointments following hospitalization for heart failure: insights from an urban medical center in the United States. Clinical Interventions in Aging. Volume 11. 1325–1332. 9 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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