Jay Patel

1.5k total citations
31 papers, 725 citations indexed

About

Jay Patel is a scholar working on Radiology, Nuclear Medicine and Imaging, Genetics and Pulmonary and Respiratory Medicine. According to data from OpenAlex, Jay Patel has authored 31 papers receiving a total of 725 indexed citations (citations by other indexed papers that have themselves been cited), including 16 papers in Radiology, Nuclear Medicine and Imaging, 7 papers in Genetics and 7 papers in Pulmonary and Respiratory Medicine. Recurrent topics in Jay Patel's work include Radiomics and Machine Learning in Medical Imaging (10 papers), Glioma Diagnosis and Treatment (7 papers) and MRI in cancer diagnosis (5 papers). Jay Patel is often cited by papers focused on Radiomics and Machine Learning in Medical Imaging (10 papers), Glioma Diagnosis and Treatment (7 papers) and MRI in cancer diagnosis (5 papers). Jay Patel collaborates with scholars based in United States, India and Switzerland. Jay Patel's co-authors include Prateek Prasanna, Sasan Partovi, Pallavi Tiwari, Anant Madabhushi, Jayashree Kalpathy–Cramer, Ken Chang, Katharina Hoebel, Andrew Beers, Niha Beig and Vinay Varadan and has published in prestigious journals such as Journal of Clinical Oncology, SHILAP Revista de lepidopterología and Neurology.

In The Last Decade

Jay Patel

27 papers receiving 719 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Jay Patel United States 15 447 205 179 115 96 31 725
Karoline Skogen Norway 9 488 1.1× 152 0.7× 149 0.8× 85 0.7× 127 1.3× 23 675
Inpyeong Hwang South Korea 17 511 1.1× 114 0.6× 204 1.1× 93 0.8× 160 1.7× 64 938
Jung Youn Kim South Korea 8 473 1.1× 172 0.8× 166 0.9× 113 1.0× 38 0.4× 20 590
Houman Sotoudeh United States 17 331 0.7× 130 0.6× 129 0.7× 71 0.6× 118 1.2× 74 814
Hwan-ho Cho South Korea 15 683 1.5× 174 0.8× 219 1.2× 122 1.1× 41 0.4× 29 819
Anahita Fathi Kazerooni United States 16 396 0.9× 209 1.0× 58 0.3× 84 0.7× 68 0.7× 65 606
Sohi Bae South Korea 11 681 1.5× 489 2.4× 184 1.0× 109 0.9× 42 0.4× 20 895
Sandra Canale France 13 297 0.7× 134 0.7× 169 0.9× 139 1.2× 121 1.3× 36 712
Jing Qi China 13 549 1.2× 154 0.8× 245 1.4× 210 1.8× 93 1.0× 46 934
Emine Şebnem Durmaz Türkiye 15 672 1.5× 127 0.6× 349 1.9× 182 1.6× 102 1.1× 26 915

Countries citing papers authored by Jay Patel

Since Specialization
Citations

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

Fields of papers citing papers by Jay Patel

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Jay Patel

This figure shows the co-authorship network connecting the top 25 collaborators of Jay Patel. A scholar is included among the top collaborators of Jay Patel 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 Jay Patel. Jay Patel 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.
Patel, Jay, et al.. (2024). PromptArt: AI-Powered Image Generation. 1–6.
2.
Kim, Albert, Jay Patel, William J. Liu, et al.. (2023). NIMG-76. A DEEP LEARNING ALGORITHM FOR FULLY AUTOMATED VOLUMETRIC MEASUREMENT OF MENINGIOMA BURDEN. Neuro-Oncology. 25(Supplement_5). v203–v203.
3.
Patel, Jay, Bernardo C. Bizzo, Daniel I. Glazer, et al.. (2022). Machine Learning for Adrenal Gland Segmentation and Classification of Normal and Adrenal Masses at CT. Radiology. 306(2). e220101–e220101. 23 indexed citations
4.
Gidwani, Mishka, Ken Chang, Jay Patel, et al.. (2022). Inconsistent Partitioning and Unproductive Feature Associations Yield Idealized Radiomic Models. Radiology. 307(1). e220715–e220715. 21 indexed citations
5.
Gidwani, Mishka, et al.. (2022). Defining the optimal millimeter threshold for target lesion inclusion in the Response Assessment in Neuro-Oncology for Brain Metastases (RANO-BM) based on outcome prediction.. Journal of Clinical Oncology. 40(16_suppl). e14003–e14003. 1 indexed citations
6.
7.
Kalpathy–Cramer, Jayashree, Jay Patel, Christopher P. Bridge, & Ken Chang. (2021). Basic Artificial Intelligence Techniques. Radiologic Clinics of North America. 59(6). 941–954. 7 indexed citations
8.
Chang, Ken, Andrew Beers, Jay Patel, et al.. (2020). Multi-Institutional Assessment and Crowdsourcing Evaluation of Deep Learning for Automated Classification of Breast Density. Journal of the American College of Radiology. 17(12). 1653–1662. 37 indexed citations
9.
Hoebel, Katharina, Vincent Andrearczyk, Andrew Beers, et al.. (2020). An exploration of uncertainty information for segmentation quality assessment. ArODES (HES-SO (https://www.hes-so.ch/)). 55–55. 20 indexed citations
10.
Prakash, Sanjay, et al.. (2019). A long-term prospective observational study in 31 patients with hemicrania continua. SHILAP Revista de lepidopterología. 2. 5 indexed citations
11.
12.
Silva, Michael A., Jay Patel, Vasileios K. Kavouridis, et al.. (2019). Machine Learning Models can Detect Aneurysm Rupture and Identify Clinical Features Associated with Rupture. World Neurosurgery. 131. e46–e51. 50 indexed citations
13.
14.
Prakash, Sanjay, et al.. (2018). Refining the clinical features of serotonin syndrome: A prospective observational study of 45 patients. Annals of Indian Academy of Neurology. 22(1). 52–52. 14 indexed citations
15.
Carrino, John A., et al.. (2018). Applications of PET/CT and PET/MR Imaging in Primary Bone Malignancies. PET Clinics. 13(4). 623–634. 36 indexed citations
16.
Beig, Niha, Jay Patel, Prateek Prasanna, et al.. (2018). Radiogenomic analysis of hypoxia pathway is predictive of overall survival in Glioblastoma. Scientific Reports. 8(1). 7–7. 114 indexed citations
17.
Mittal, Pardeep, Argha Chatterjee, Deborah A. Baumgarten, et al.. (2018). Spectrum of Extratesticular and Testicular Pathologic Conditions at Scrotal MR Imaging. Radiographics. 38(3). 806–830. 29 indexed citations
18.
Prasanna, Prateek, Jay Patel, Sasan Partovi, Anant Madabhushi, & Pallavi Tiwari. (2016). Radiomic features from the peritumoral brain parenchyma on treatment-naïve multi-parametric MR imaging predict long versus short-term survival in glioblastoma multiforme: Preliminary findings. European Radiology. 27(10). 4188–4197. 213 indexed citations
19.
Kesler, Kenneth A., Jay Patel, Thomas J. Birdas, et al.. (2012). The “growing teratoma syndrome” in primary mediastinal nonseminomatous germ cell tumors: Criteria based on current practice. Journal of Thoracic and Cardiovascular Surgery. 144(2). 438–443. 26 indexed citations
20.
Ostuni, John, Nancy Richert, Bobbi K. Lewis, et al.. (2002). Comparison of methods for obtaining longitudinal whole‐brain magnetization transfer measurements. Journal of Magnetic Resonance Imaging. 15(1). 8–15. 1 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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