Ankur M. Doshi

1.3k total citations
60 papers, 973 citations indexed

About

Ankur M. Doshi is a scholar working on Radiology, Nuclear Medicine and Imaging, Pulmonary and Respiratory Medicine and Surgery. According to data from OpenAlex, Ankur M. Doshi has authored 60 papers receiving a total of 973 indexed citations (citations by other indexed papers that have themselves been cited), including 42 papers in Radiology, Nuclear Medicine and Imaging, 24 papers in Pulmonary and Respiratory Medicine and 9 papers in Surgery. Recurrent topics in Ankur M. Doshi's work include Radiomics and Machine Learning in Medical Imaging (16 papers), MRI in cancer diagnosis (15 papers) and Radiology practices and education (15 papers). Ankur M. Doshi is often cited by papers focused on Radiomics and Machine Learning in Medical Imaging (16 papers), MRI in cancer diagnosis (15 papers) and Radiology practices and education (15 papers). Ankur M. Doshi collaborates with scholars based in United States, Canada and Japan. Ankur M. Doshi's co-authors include Andrew B. Rosenkrantz, Hersh Chandarana, Michael P. Recht, James S. Babb, Justin Ream, Samir S. Taneja, Dana J. Lin, Soterios Gyftopoulos, Florian Knöll and Tatiane Cantarelli Rodrigues and has published in prestigious journals such as Journal of Clinical Oncology, Neurology and Radiology.

In The Last Decade

Ankur M. Doshi

57 papers receiving 951 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Ankur M. Doshi United States 19 544 322 175 130 101 60 973
Michael A. Bruno United States 19 728 1.3× 112 0.3× 145 0.8× 51 0.4× 164 1.6× 74 1.2k
Hanna M. Zafar United States 18 461 0.8× 107 0.3× 121 0.7× 60 0.5× 60 0.6× 65 876
Bhushan Desai United States 21 718 1.3× 631 2.0× 104 0.6× 81 0.6× 147 1.5× 48 1.1k
Sharon W. Kwan United States 19 385 0.7× 394 1.2× 451 2.6× 165 1.3× 109 1.1× 50 1.6k
Idalid Franco United States 13 639 1.2× 599 1.9× 97 0.6× 69 0.5× 181 1.8× 58 1.3k
Hayan Jouni United States 16 146 0.3× 123 0.4× 162 0.9× 194 1.5× 62 0.6× 48 953
Larry Goldenberg Canada 21 213 0.4× 770 2.4× 249 1.4× 234 1.8× 132 1.3× 64 1.5k
Jadranka Stojanovska United States 22 650 1.2× 240 0.7× 155 0.9× 63 0.5× 95 0.9× 69 1.2k
Mina S. Makary United States 17 236 0.4× 157 0.5× 220 1.3× 57 0.4× 104 1.0× 123 983
Nak‐Hoon Son South Korea 17 228 0.4× 234 0.7× 140 0.8× 102 0.8× 50 0.5× 86 904

Countries citing papers authored by Ankur M. Doshi

Since Specialization
Citations

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

Fields of papers citing papers by Ankur M. Doshi

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Ankur M. Doshi. 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 Ankur M. Doshi. The network helps show where Ankur M. Doshi may publish in the future.

Co-authorship network of co-authors of Ankur M. Doshi

This figure shows the co-authorship network connecting the top 25 collaborators of Ankur M. Doshi. A scholar is included among the top collaborators of Ankur M. Doshi 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 Ankur M. Doshi. Ankur M. Doshi 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.
Block, Kai Tobias, et al.. (2024). Patterns of Access to Radiology Reports and Images Through a Patient Portal. Journal of Imaging Informatics in Medicine. 37(2). 504–509. 1 indexed citations
2.
Doshi, Ankur M., et al.. (2023). Fast and Frictionless: A Novel Approach to Radiology Appointment Scheduling Using a Mobile App and Recommendation Engine. Journal of Digital Imaging. 36(4). 1285–1290. 3 indexed citations
3.
Schieda, Nicola, Matthew S. Davenport, Stuart G. Silverman, et al.. (2022). Multicenter Evaluation of Multiparametric MRI Clear Cell Likelihood Scores in Solid Indeterminate Small Renal Masses. Radiology. 303(3). 590–599. 36 indexed citations
4.
Chang, Gregory, Ankur M. Doshi, Hersh Chandarana, & Michael P. Recht. (2021). Impact of COVID-19 Workflow Changes on Patient Throughput at Outpatient Imaging Centers. Academic Radiology. 28(3). 297–306. 11 indexed citations
5.
Schieda, Nicola, Matthew S. Davenport, Iván Pedrosa, et al.. (2019). Renal and adrenal masses containing fat at MRI: Proposed nomenclature by the society of abdominal radiology disease‐focused panel on renal cell carcinoma. Journal of Magnetic Resonance Imaging. 49(4). 917–926. 27 indexed citations
6.
Moore, William H., Ankur M. Doshi, Soterios Gyftopoulos, et al.. (2019). Enhancing communication in radiology using a hybrid computer-human based system. Clinical Imaging. 61. 95–98.
7.
Davenport, Matthew S., Hersh Chandarana, Nicole E. Curci, et al.. (2018). Society of Abdominal Radiology disease-focused panel on renal cell carcinoma: update on past, current, and future goals. Abdominal Radiology. 43(9). 2213–2220. 3 indexed citations
8.
9.
Smereka, Paul, Ankur M. Doshi, Justin Ream, & Andrew B. Rosenkrantz. (2017). The American College of Radiology Incidental Findings Committee Recommendations for Management of Incidental Lymph Nodes. Academic Radiology. 24(5). 603–608. 2 indexed citations
10.
Kim, Daniel, et al.. (2016). A Multidisciplinary Approach to Improving Appropriate Follow-Up Imaging of Ovarian Cysts: A Quality Improvement Initiative. Journal of the American College of Radiology. 13(5). 535–541. 19 indexed citations
11.
Chandarana, Hersh, Ankur M. Doshi, Krishna Shanbhogue, et al.. (2016). Three-dimensional MR Cholangiopancreatography in a Breath Hold with Sparsity-based Reconstruction of Highly Undersampled Data. Radiology. 280(2). 585–594. 54 indexed citations
12.
Rosenkrantz, Andrew B., Ankur M. Doshi, Luke Ginocchio, & Yindalon Aphinyanaphongs. (2016). Use of a Machine-learning Method for Predicting Highly Cited Articles Within General Radiology Journals. Academic Radiology. 23(12). 1573–1581. 2 indexed citations
13.
14.
Doshi, Ankur M., et al.. (2016). Strengths and Deficiencies in the Content of US Radiology Private Practices’ Websites. Journal of the American College of Radiology. 14(3). 431–435. 10 indexed citations
15.
Rosenkrantz, Andrew B., et al.. (2016). Most Common Publication Types in Radiology Journals:. Academic Radiology. 23(5). 628–633. 8 indexed citations
16.
17.
Doshi, Ankur M., et al.. (2015). Factors Influencing Patients’ Perspectives of Radiology Imaging Centers: Evaluation Using an Online Social Media Ratings Website. Journal of the American College of Radiology. 13(2). 210–216. 19 indexed citations
18.
Kansagra, Akash P., John‐Paul J. Yu, Arindam Chatterjee, et al.. (2015). Big Data and the Future of Radiology Informatics. Academic Radiology. 23(1). 30–42. 51 indexed citations
19.
Rosenkrantz, Andrew B. & Ankur M. Doshi. (2014). Characterizing the Performance of the Nation’s Hospitals in the Hospital Outpatient Quality Reporting Program’s Imaging Efficiency Measures. Journal of the American College of Radiology. 12(2). 166–173. 16 indexed citations
20.
Rosenkrantz, Andrew B., Genevieve L. Bennett, Ankur M. Doshi, et al.. (2014). T2-weighted imaging of the prostate: Impact of the BLADE technique on image quality and tumor assessment. Abdominal Imaging. 40(3). 552–559. 27 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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