Dania Daye

2.8k total citations
98 papers, 1.9k citations indexed

About

Dania Daye is a scholar working on Radiology, Nuclear Medicine and Imaging, Pulmonary and Respiratory Medicine and Surgery. According to data from OpenAlex, Dania Daye has authored 98 papers receiving a total of 1.9k indexed citations (citations by other indexed papers that have themselves been cited), including 47 papers in Radiology, Nuclear Medicine and Imaging, 22 papers in Pulmonary and Respiratory Medicine and 17 papers in Surgery. Recurrent topics in Dania Daye's work include Radiomics and Machine Learning in Medical Imaging (25 papers), Radiology practices and education (20 papers) and Diversity and Career in Medicine (17 papers). Dania Daye is often cited by papers focused on Radiomics and Machine Learning in Medical Imaging (25 papers), Radiology practices and education (20 papers) and Diversity and Career in Medicine (17 papers). Dania Daye collaborates with scholars based in United States, Germany and Mexico. Dania Daye's co-authors include Kathryn E. Wellen, Tobias Walker, Despina Kontos, Carolyn Mies, Ahmed Ashraf, Mark Rosen, Efrén J. Flores, Sara C. Gavenonis, Michael D. Feldman and McKinley Glover and has published in prestigious journals such as Journal of Clinical Investigation, Journal of Clinical Oncology and SHILAP Revista de lepidopterología.

In The Last Decade

Dania Daye

88 papers receiving 1.8k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Dania Daye United States 22 704 388 314 273 272 98 1.9k
Jean‐Emmanuel Bibault France 25 921 1.3× 526 1.4× 105 0.3× 140 0.5× 309 1.1× 100 2.1k
Michael Montalto United States 24 409 0.6× 200 0.5× 551 1.8× 638 2.3× 475 1.7× 95 2.9k
Alexis B. Carter United States 20 333 0.5× 194 0.5× 281 0.9× 402 1.5× 627 2.3× 50 1.9k
Daniel W. Golden United States 23 587 0.8× 424 1.1× 124 0.4× 200 0.7× 78 0.3× 145 2.0k
Benjamin H. Kann United States 22 949 1.3× 503 1.3× 101 0.3× 172 0.6× 438 1.6× 96 2.2k
Elaine Lee Hong Kong 23 1.9k 2.6× 613 1.6× 126 0.4× 276 1.0× 415 1.5× 110 3.6k
Sonal Gandhi Canada 28 676 1.0× 180 0.5× 416 1.3× 145 0.5× 317 1.2× 96 1.9k
Richard G. Abramson United States 29 1.3k 1.8× 339 0.9× 215 0.7× 345 1.3× 233 0.9× 101 2.7k
Sven H. Loosen Germany 21 492 0.7× 340 0.9× 463 1.5× 446 1.6× 429 1.6× 88 2.3k
Ronilda Lacson United States 23 808 1.1× 395 1.0× 58 0.2× 317 1.2× 452 1.7× 107 1.8k

Countries citing papers authored by Dania Daye

Since Specialization
Citations

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

Fields of papers citing papers by Dania Daye

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Dania Daye

This figure shows the co-authorship network connecting the top 25 collaborators of Dania Daye. A scholar is included among the top collaborators of Dania Daye 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 Dania Daye. Dania Daye 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.
Tripathi, Satvik, Azadeh Tabari, Bernardo C. Bizzo, et al.. (2025). PRECISE framework: Enhanced radiology reporting with GPT for improved readability, reliability, and patient-centered care. European Journal of Radiology. 187. 112124–112124. 1 indexed citations
2.
3.
Daye, Dania, et al.. (2025). Under Pressure: Treatment of Acute Severe Hypertension (Hypertensive Crisis). Techniques in vascular and interventional radiology. 28(2). 101040–101040.
4.
Christophers, Briana, et al.. (2024). Sociodemographic factors and research experience impact MD-PhD program acceptance. JCI Insight. 9(3). 1 indexed citations
5.
Ramasamy, Shakthi Kumaran, et al.. (2024). Enhanced PROcedural Information READability for Patient-Centered Care in Interventional Radiology With Large Language Models (PRO-READ IR). Journal of the American College of Radiology. 22(1). 84–97. 6 indexed citations
6.
Mansur, Arian, Omar M. Omar, Khalid Ahmed, et al.. (2024). Beyond MELD Score: Association of Machine Learning-derived CT Body Composition with 90-Day Mortality Post Transjugular Intrahepatic Portosystemic Shunt Placement. CardioVascular and Interventional Radiology. 48(2). 221–230. 2 indexed citations
7.
Tripathi, Satvik, et al.. (2024). From Machine Learning to Patient Outcomes: A Comprehensive Review of AI in Pancreatic Cancer. Diagnostics. 14(2). 174–174. 22 indexed citations
8.
9.
Carey, Denston, Avik Som, John Di Capua, et al.. (2023). Management of hemoptysis with bronchial artery embolization: Benign versus malignant indications. 7. 3–3. 1 indexed citations
10.
Mansur, Arian, et al.. (2023). Role of Machine Learning-Based CT Body Composition in Risk Prediction and Prognostication: Current State and Future Directions. Diagnostics. 13(5). 968–968. 17 indexed citations
11.
Hernandez‐Barco, Yasmin G., Dania Daye, Carlos Fernández‐del Castillo, et al.. (2023). IPMN-LEARN: A linear support vector machine learning model for predicting low-grade intraductal papillary mucinous neoplasms. Annals of Hepato-Biliary-Pancreatic Surgery. 27(2). 195–200. 9 indexed citations
12.
13.
Christophers, Briana, Yentli E. Soto Albrecht, Rachit Kumar, et al.. (2022). The Virtual Summer Research Program: supporting future physician-scientists from underrepresented backgrounds. Journal of Clinical and Translational Science. 6(1). e120–e120. 4 indexed citations
14.
Daye, Dania, Walter F. Wiggins, Matthew P. Lungren, et al.. (2022). Implementation of Clinical Artificial Intelligence in Radiology: Who Decides and How?. Radiology. 305(3). 555–563. 70 indexed citations
15.
Kwan, Jennifer M., Evan Noch, Yuqing Qiu, et al.. (2022). The Impact of COVID-19 on Physician–Scientist Trainees and Faculty in the United States: A National Survey. Academic Medicine. 97(10). 1536–1545. 12 indexed citations
16.
Reid, Nicholas, John Panagides, John Di Capua, et al.. (2022). Interpretable Machine Learning for the Prediction of Amputation Risk Following Lower Extremity Infrainguinal Endovascular Interventions for Peripheral Arterial Disease. CardioVascular and Interventional Radiology. 45(5). 633–640. 14 indexed citations
17.
Glover, McKinley, Dania Daye, P. Jones, et al.. (2018). Implementation of Digital Awareness Strategies to Engage Patients and Providers in a Lung Cancer Screening Program: Retrospective Study. Journal of Medical Internet Research. 20(2). e52–e52. 43 indexed citations
18.
Kohli, Marc, Dania Daye, Alexander J. Towbin, Amy L. Kotsenas, & Marta E. Heilbrun. (2018). Social Media Tools for Department and Practice Communication and Branding in the Digital Age. Radiographics. 38(6). 1773–1785. 29 indexed citations
19.
Daye, Dania & Tobias Walker. (2018). Complications of endovascular aneurysm repair of the thoracic and abdominal aorta: evaluation and management. Cardiovascular Diagnosis and Therapy. 8(S1). S138–S156. 134 indexed citations
20.
Ashraf, Ahmed, Sara C. Gavenonis, Dania Daye, et al.. (2012). A Multichannel Markov Random Field Framework for Tumor Segmentation With an Application to Classification of Gene Expression-Based Breast Cancer Recurrence Risk. IEEE Transactions on Medical Imaging. 32(4). 637–648. 60 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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