Geunwon Kim

26 papers receiving 476 citations

Peers

Geunwon Kim
Comparison fields: 5 of 82
  • Aging 16
  • Cell Biology 84
  • Cancer Research 75
  • Oncology 135
  • Radiology, Nuclear Medicine and Imaging 104
Replace Johann Schönberger with:
Johann Schönberger Germany
Yanjun Shi China
Shayan Nazari United States
Søren M. Johnson United States
Claudia Ligorio Italy
Bhawana George United States
Fujun Han China
Beth K. Neilsen United States
Dolores Mahmud United States
Geunwon Kim relative to Johann Schönberger Germany Johann Schönberger's profile →
Citations per field
00.5×10×15×19.5×
Johann Schönberger · 1×
Citations per year

Countries citing papers authored by Geunwon Kim

Since Specialization
Citations

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

Fields of papers citing papers by Geunwon Kim

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

The 25 scholars most cited alongside Geunwon Kim, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Geunwon Kim Line = papers co-authored together Geunwon Kim links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 27 papers — load more, or switch the sort, to bring in the rest.

#Work
1 201293
2 202164
3 201262
4 202029
5 201628
6 201927
7 200122
8 202222
9 202017
10 200113
11 202213
12 201913
13 202112
14 202111
15
Spontaneous regression of a tectal mass in neurofibromatosis 1.
199811
16 201710
17 20207
18 20216
19 20214
20 20224

About Geunwon Kim

Geunwon Kim is a scholar working on Pulmonary and Respiratory Medicine, Oncology, Surgery, Radiology, Nuclear Medicine and Imaging and Artificial Intelligence, having authored 27 papers that have together received 486 indexed citations. Recurring topics across this work include Lymphatic System and Diseases (7 papers), Diagnosis and Treatment of Venous Diseases (7 papers), Digital Radiography and Breast Imaging (6 papers), AI in cancer detection (5 papers), Breast Cancer Treatment Studies (4 papers), Heat shock proteins research (3 papers), Myofascial pain diagnosis and treatment (3 papers) and Breast Lesions and Carcinomas (3 papers). The work is most often cited by research in Aging (16 citations), Cell Biology (84 citations), Cancer Research (75 citations), Oncology (135 citations) and Radiology, Nuclear Medicine and Imaging (104 citations). Geunwon Kim has collaborated with scholars based in United States, South Korea and Canada. Frequent co-authors include Manisha Bahl, Vladimir L. Gabai, Ivor J. Benjamin, Michael Y. Sherman, Le Meng, Dhruv Singhal, Leo L. Tsai, Elisabeth Christians, Anatoli B. Meriin and Anna Rose Johnson. Their work appears in journals such as Academic Radiology, European Radiology, American Journal of Neuroradiology, Radiology and Lymphatic Research and Biology.

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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