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.
Digital technology, tele-medicine and artificial intelligence in ophthalmology: A global perspective
2020358 citationsJi-Peng Olivia Li, Hanruo Liu et al.Progress in Retinal and Eye Researchprofile →
Peers — A (Enhanced Table)
Peers by citation overlap · career bar shows stage (early→late)
cites ·
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This map shows the geographic impact of Judy E. Kim'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 Judy E. Kim with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Judy E. Kim more than expected).
This network shows the impact of papers produced by Judy E. Kim. 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 Judy E. Kim. The network helps show where Judy E. Kim may publish in the future.
Co-authorship network of co-authors of Judy E. Kim
This figure shows the co-authorship network connecting the top 25 collaborators of Judy E. Kim.
A scholar is included among the top collaborators of Judy E. Kim 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 Judy E. Kim. Judy E. Kim is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Kim, Judy E., et al.. (2021). Pentosan Polysulfate Sodium Maculopathy: A Big Data Study of Patients with Interstitial Cystitis. Investigative Ophthalmology & Visual Science. 62(8). 168–168.1 indexed citations
8.
Lally, David R., et al.. (2020). Performance of a novel deep learning algorithm for Automatic Retinal Fluid Quantification in Home OCT Images. Investigative Ophthalmology & Visual Science. 61(7). 2571–2571.5 indexed citations
9.
Li, Ji-Peng Olivia, Hanruo Liu, Darren Shu Jeng Ting, et al.. (2020). Digital technology, tele-medicine and artificial intelligence in ophthalmology: A global perspective. Progress in Retinal and Eye Research. 82. 100900–100900.358 indexed citations breakdown →
10.
Kim, Judy E., et al.. (2019). The acceptance of teleophthalmology in community health settings in Milwaukee. Investigative Ophthalmology & Visual Science. 60(9). 1092–1092.2 indexed citations
11.
Eells, Janis T., Sandeep Gopalakrishnan, Thomas B. Connor, et al.. (2017). 670 nm Photobiomodulation as a Therapy for Diabetic Macular Edema: A Pilot Study. Investigative Ophthalmology & Visual Science. 58(8). 932–932.6 indexed citations
12.
Kim, Judy E., et al.. (2015). Analysis of Malpractice Claims Filed Against Retina Specialists Based on Practice Location: Is There a Litigious Trend?. Investigative Ophthalmology & Visual Science. 56(7). 2147–2147.1 indexed citations
13.
Hahn, Paul, Mina Chung, Harry W. Flynn, et al.. (2014). Safety profile of ocriplasmin for symptomatic vitreomacular adhesion - a comprehensive analysis of pre- and post-marketing experiences. Investigative Ophthalmology & Visual Science. 55(13). 2209–2209.2 indexed citations
14.
Scoles, Drew, Christopher S. Langlo, Yusufu N. Sulai, et al.. (2014). Non-invasive evaluation of microscopic retinal pathology in macular telangiectasia type 2. Investigative Ophthalmology & Visual Science. 55(13). 5951–5951.1 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.