Neil Joshi

1.9k total citations · 1 hit paper
24 papers, 1.2k citations indexed

About

Neil Joshi is a scholar working on Radiology, Nuclear Medicine and Imaging, Ophthalmology and Artificial Intelligence. According to data from OpenAlex, Neil Joshi has authored 24 papers receiving a total of 1.2k indexed citations (citations by other indexed papers that have themselves been cited), including 12 papers in Radiology, Nuclear Medicine and Imaging, 10 papers in Ophthalmology and 6 papers in Artificial Intelligence. Recurrent topics in Neil Joshi's work include Retinal Imaging and Analysis (11 papers), Retinal Diseases and Treatments (9 papers) and Retinal and Optic Conditions (6 papers). Neil Joshi is often cited by papers focused on Retinal Imaging and Analysis (11 papers), Retinal Diseases and Treatments (9 papers) and Retinal and Optic Conditions (6 papers). Neil Joshi collaborates with scholars based in United States, Australia and Canada. Neil Joshi's co-authors include Philippe Burlina, Neil M. Bressler, Kátia D. Pacheco, David Freund, Michael Pekala, Seth Billings, Jemima Albayda, Jun Kong, William Paul and T. Y. Alvin Liu and has published in prestigious journals such as Journal of the American College of Cardiology, PLoS ONE and Investigative Ophthalmology & Visual Science.

In The Last Decade

Neil Joshi

21 papers receiving 1.2k citations

Hit Papers

Automated Grading of Age-Related Macular Degeneration Fro... 2017 2026 2020 2023 2017 100 200 300 400
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.

  1. Automated Grading of Age-Related Macular Degeneration From Color Fundus Images Using Deep Convolutional Neural Networks
    2017 426 citations Philippe Burlina, Neil Joshi et al. JAMA Ophthalmology profile →

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Neil Joshi United States 13 886 668 230 197 111 24 1.2k
Kátia D. Pacheco United States 10 860 1.0× 703 1.1× 199 0.9× 127 0.6× 96 0.9× 20 1.1k
Edward Korot United Kingdom 14 760 0.9× 550 0.8× 112 0.5× 219 1.1× 205 1.8× 38 1.2k
Avinash V. Varadarajan United States 8 1.1k 1.2× 732 1.1× 181 0.8× 298 1.5× 219 2.0× 10 1.6k
Carson Lam United States 13 606 0.7× 290 0.4× 179 0.8× 275 1.4× 92 0.8× 25 1.1k
Siamak Yousefi United States 22 1.7k 2.0× 1.7k 2.5× 205 0.9× 75 0.4× 115 1.0× 85 2.1k
Yuchen Xie China 12 497 0.6× 358 0.5× 109 0.5× 79 0.4× 95 0.9× 26 748
Kyu-Hwan Jung South Korea 18 679 0.8× 232 0.3× 226 1.0× 264 1.3× 78 0.7× 38 1.1k
Adrián Colomer Spain 16 440 0.5× 226 0.3× 293 1.3× 323 1.6× 24 0.2× 56 796
Qianzhong Cao China 13 325 0.4× 324 0.5× 64 0.3× 80 0.4× 64 0.6× 37 648
Qiaoliang Li China 14 599 0.7× 329 0.5× 448 1.9× 99 0.5× 19 0.2× 49 985

Countries citing papers authored by Neil Joshi

Since Specialization
Citations

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

Fields of papers citing papers by Neil Joshi

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Neil Joshi

This figure shows the co-authorship network connecting the top 25 collaborators of Neil Joshi. A scholar is included among the top collaborators of Neil Joshi 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 Neil Joshi. Neil Joshi 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.
Zhou, Ashley, William Paul, Philippe Burlina, et al.. (2025). Estimating Visual Acuity With Spectacle Correction From Fundus Photos Using Artificial Intelligence. JAMA Network Open. 8(1). e2453770–e2453770. 2 indexed citations
2.
Paul, William, Philippe Burlina, Neil Joshi, et al.. (2023). Accuracy of Artificial Intelligence in Estimating Best-Corrected Visual Acuity From Fundus Photographs in Eyes With Diabetic Macular Edema. JAMA Ophthalmology. 141(7). 677–677. 11 indexed citations
3.
Joshi, Neil, et al.. (2022). Assessment of germinal matrix hemorrhage on head ultrasound with deep learning algorithms. Pediatric Radiology. 52(3). 533–538. 4 indexed citations
4.
5.
Burlina, Philippe, Neil Joshi, William Paul, Kátia D. Pacheco, & Neil M. Bressler. (2021). Addressing Artificial Intelligence Bias in Retinal Diagnostics. Translational Vision Science & Technology. 10(2). 13–13. 76 indexed citations
6.
Burlina, Philippe, et al.. (2020). AI-based detection of erythema migrans and disambiguation against other skin lesions. Computers in Biology and Medicine. 125. 103977–103977. 17 indexed citations
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10.
Burlina, Philippe, Neil Joshi, Kátia D. Pacheco, T. Y. Alvin Liu, & Neil M. Bressler. (2019). Assessment of Deep Generative Models for High-Resolution Synthetic Retinal Image Generation of Age-Related Macular Degeneration. JAMA Ophthalmology. 137(3). 258–258. 110 indexed citations
11.
Burlina, Philippe, Neil Joshi, Elise Ng, et al.. (2018). Automated detection of erythema migrans and other confounding skin lesions via deep learning. Computers in Biology and Medicine. 105. 151–156. 43 indexed citations
12.
Burlina, Philippe, Neil Joshi, Seth Billings, I-Jeng Wang, & Jemima Albayda. (2018). Deep embeddings for novelty detection in myopathy. Computers in Biology and Medicine. 105. 46–53. 18 indexed citations
13.
Burlina, Philippe, Seth Billings, Neil Joshi, & Jemima Albayda. (2017). Automated diagnosis of myositis from muscle ultrasound: Exploring the use of machine learning and deep learning methods. PLoS ONE. 12(8). e0184059–e0184059. 107 indexed citations
14.
Burlina, Philippe, Neil Joshi, Michael Pekala, et al.. (2017). Automated Grading of Age-Related Macular Degeneration From Color Fundus Images Using Deep Convolutional Neural Networks. JAMA Ophthalmology. 135(11). 1170–1170. 426 indexed citations breakdown →
15.
Burlina, Philippe, Kátia D. Pacheco, Neil Joshi, David Freund, & Neil M. Bressler. (2017). Comparing humans and deep learning performance for grading AMD: A study in using universal deep features and transfer learning for automated AMD analysis. Computers in Biology and Medicine. 82. 80–86. 160 indexed citations
16.
Joshi, Neil, Michael Pekala, Kátia D. Pacheco, et al.. (2017). A Hybrid Approach for Incorporating Deep Visual Features and Side Channel Information with Applications to AMD Detection. 12 indexed citations
17.
Joshi, Neil, et al.. (2017). Machine Learning Methods for 1D Ultrasound Breast Cancer Screening. 33. 711–715. 2 indexed citations
18.
Pacheco, Kátia D., Yulia Wolfson, Philippe Burlina, et al.. (2016). Evaluation of automated drusen detection system for fundus photographs of patients with age-related macular degeneration. Investigative Ophthalmology & Visual Science. 57(12). 1611–1611. 4 indexed citations
19.
Patel, Achint, Neil Joshi, Pranav S. Garimella, et al.. (2016). NATIONAL TRENDS AND OUTCOMES IN DIALYSIS REQUIRING ACUTE KIDNEY INJURY AFTER PERCUTANEOUS CORONARY INTERVENTION AFTER ACUTE MYOCARDIAL INFARCTION. Journal of the American College of Cardiology. 67(13). 395–395. 1 indexed citations
20.
Joshi, Neil & Ling Guan. (2006). Missing data ASR with fusion of features and combination of recognizers. 4. 114–117.

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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