Daniel H. Kwon

36 papers receiving 369 citations

Peers

Daniel H. Kwon
Comparison fields: 5 of 64
  • Health Informatics 56
  • Oncology 87
  • Radiology, Nuclear Medicine and Imaging 58
  • Immunology and Allergy 15
  • Pulmonary and Respiratory Medicine 72
Replace Ameish Govindarajan with:
Ameish Govindarajan United States
Jeremy Chang United States
Nikolas Schopow Germany
Cundong Liu China
Gareth Bryson United Kingdom
Benjamin Harris United Kingdom
David Yao United States
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Daniel H. Kwon relative to Ameish Govindarajan United States Ameish Govindarajan's profile →
Citations per field
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Citations per year

Countries citing papers authored by Daniel H. Kwon

Since Specialization
Citations

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

Fields of papers citing papers by Daniel H. Kwon

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Daniel H. Kwon. 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 Daniel H. Kwon. The network helps show where Daniel H. Kwon may publish in the future.

Co-authors

The 25 scholars most cited alongside Daniel H. Kwon, 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 Daniel H. Kwon Line = papers co-authored together Daniel H. Kwon links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 42 papers — load more, or switch the sort, to bring in the rest.

#Work
1 202360
2 201450
3 201947
4 200133
5 202127
6 201924
7 202420
8 202114
9 202012
10 202111
11 20239
12 20246
13 20215
14 20215
15 20145
16 20205
17 20175
18 20224
19 20204
20 20224

About Daniel H. Kwon

Daniel H. Kwon is a scholar working on Oncology, Pulmonary and Respiratory Medicine, Surgery, Radiology, Nuclear Medicine and Imaging and Molecular Biology, having authored 42 papers that have together received 382 indexed citations. Recurring topics across this work include Prostate Cancer Treatment and Research (9 papers), Bladder and Urothelial Cancer Treatments (7 papers), Cancer Immunotherapy and Biomarkers (6 papers), COVID-19 and healthcare impacts (4 papers), Urinary and Genital Oncology Studies (4 papers), BRCA gene mutations in cancer (3 papers), Neuroendocrine Tumor Research Advances (3 papers) and Radiopharmaceutical Chemistry and Applications (3 papers). The work is most often cited by research in Health Informatics (56 citations), Oncology (87 citations), Radiology, Nuclear Medicine and Imaging (58 citations), Immunology and Allergy (15 citations) and Pulmonary and Respiratory Medicine (72 citations). Daniel H. Kwon has collaborated with scholars based in United States, South Korea and Canada. Frequent co-authors include Eric J. Small, Hala T. Borno, Tahereh Ghaziani, Paul Y. Kwo, Andrew Kesselman, Justin R. Tse, Aya Kamaya, Gary Tse, Alicia Y. Zhou and Heather H. Cheng. Their work appears in journals such as Journal of Clinical Oncology, Urologic Oncology Seminars and Original Investigations, JCO Oncology Practice, Cancer Medicine and Cancer.

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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