Hong‐Jun Yoon
Impact in
- Health Informatics top 5%
- Artificial Intelligence top 2%
- AI in cancer detection
- Topic Modeling
- Machine Learning in Healthcare
Papers in
-
- AI in cancer detection 20
- Topic Modeling 15
- Natural Language Processing Techniques 6
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- Radiomics and Machine Learning in Medical Imaging 9
- Radiology practices and education 4
- Co-authors
- Georgia D. Tourassi (45 shared papers)John X. Qiu (7 shared papers)Paul Fearn (4 shared papers)Shang Gao (13 shared papers)Mohammed Alawad (10 shared papers)Xiao‐Cheng Wu (13 shared papers)D.C. Yu (2 shared papers)L. Kojovic (2 shared papers)
- Journals
- Journal of Biomedical Informatics (4 papers)IEEE Journal of Biomedical and Health Informatics (3 papers)Academic Radiology (3 papers)Medical Physics (2 papers)Journal of the American Medical Informatics Association (2 papers)
- Partner nations
- United StatesSouth KoreaJapan
In The Last Decade
Hong‐Jun Yoon
68 papers receiving 1.1k citations
Peers
Comparison fields: 5 of 122
- Health Informatics 41
- Artificial Intelligence 514
- Health Information Management 48
- Radiology, Nuclear Medicine and Imaging 196
- Computer Vision and Pattern Recognition 76
Countries citing papers authored by Hong‐Jun Yoon
This map shows the geographic impact of Hong‐Jun Yoon'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 Hong‐Jun Yoon with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Hong‐Jun Yoon more than expected).
Fields of papers citing papers by Hong‐Jun Yoon
This network shows the impact of papers produced by Hong‐Jun Yoon. 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 Hong‐Jun Yoon. The network helps show where Hong‐Jun Yoon may publish in the future.
Co-authors
The 25 scholars most cited alongside Hong‐Jun Yoon, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
Showing the 20 most-cited of 74 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | 2021 | 107 | |
| 2 | 2001 | 97 | |
| 3 | 2017 | 96 | |
| 4 | 2017 | 91 | |
| 5 | 2020 | 59 | |
| 6 | 2018 | 47 | |
| 7 | 2019 | 43 | |
| 8 | 2021 | 39 | |
| 9 | 2020 | 31 | |
| 10 | 2021 | 28 | |
| 11 | 2013 | 27 | |
| 12 | 1999 | 26 | |
| 13 | 2006 | 25 | |
| 14 | 2014 | 24 | |
| 15 | 2024 | 18 | |
| 16 | 2018 | 18 | |
| 17 | 2019 | 15 | |
| 18 | 2009 | 14 | |
| 19 | 2011 | 14 | |
| 20 | 2014 | 13 |
About Hong‐Jun Yoon
Hong‐Jun Yoon is a scholar working on Artificial Intelligence, Radiology, Nuclear Medicine and Imaging, Molecular Biology, Computer Vision and Pattern Recognition and Oncology, having authored 74 papers that have together received 1.1k indexed citations. Recurring topics across this work include AI in cancer detection (20 papers), Topic Modeling (15 papers), Radiomics and Machine Learning in Medical Imaging (9 papers), Biomedical Text Mining and Ontologies (9 papers), Natural Language Processing Techniques (6 papers), Medical Image Segmentation Techniques (4 papers), Radiology practices and education (4 papers) and Data-Driven Disease Surveillance (4 papers). The work is most often cited by research in Health Informatics (41 citations), Artificial Intelligence (514 citations), Health Information Management (48 citations), Radiology, Nuclear Medicine and Imaging (196 citations) and Computer Vision and Pattern Recognition (76 citations). Hong‐Jun Yoon has collaborated with scholars based in United States, South Korea and Japan. Frequent co-authors include Georgia D. Tourassi, John X. Qiu, Paul Fearn, Shang Gao, Mohammed Alawad, Xiao‐Cheng Wu, D.C. Yu, L. Kojovic, Linda Coyle and M. Todd Young. Their work appears in journals such as Journal of Biomedical Informatics, IEEE Journal of Biomedical and Health Informatics, Academic Radiology, Medical Physics and Journal of the American Medical Informatics Association.
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.