Sungwon Ham
Impact in
- Health Informatics top 5%
- Artificial Intelligence in Healthcare and Education
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- Radiomics and Machine Learning in Medical Imaging
- MRI in cancer diagnosis
Papers in
-
- Radiomics and Machine Learning in Medical Imaging 7
- MRI in cancer diagnosis 4
- Medical Imaging Techniques and Applications 2
- COVID-19 diagnosis using AI 1
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- Cerebrovascular and Carotid Artery Diseases 2
- Co-authors
- Namkug Kim (14 shared papers)Hyunna Lee (3 shared papers)Jihye Yun (7 shared papers)Ji Eun Park (2 shared papers)Ho Sung Kim (2 shared papers)Han-Bin Lee (1 shared paper)Eun‐Jae Lee (1 shared paper)Jong S. Kim (1 shared paper)
- Journals
- Scientific Reports (7 papers)Korean Journal of Radiology (3 papers)European Radiology (3 papers)La radiologia medica (1 paper)Breast Cancer Research (1 paper)
- Partner nations
- South KoreaJapanUnited States
In The Last Decade
Sungwon Ham
14 papers receiving 379 citations
Peers
Comparison fields: 5 of 66
- Health Informatics 40
- Radiology, Nuclear Medicine and Imaging 202
- Otorhinolaryngology 34
- Neurology 47
- Health Information Management 24
Countries citing papers authored by Sungwon Ham
This map shows the geographic impact of Sungwon Ham'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 Sungwon Ham with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Sungwon Ham more than expected).
Fields of papers citing papers by Sungwon Ham
This network shows the impact of papers produced by Sungwon Ham. 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 Sungwon Ham. The network helps show where Sungwon Ham may publish in the future.
Co-authors
The 25 scholars most cited alongside Sungwon Ham, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | 2020 | 130 | |
| 2 | 2019 | 77 | |
| 3 | 2020 | 44 | |
| 4 | 2020 | 39 | |
| 5 | 2020 | 30 | |
| 6 | 2024 | 22 | |
| 7 | 2023 | 12 | |
| 8 | 2021 | 9 | |
| 9 | 2022 | 8 | |
| 10 | 2022 | 6 | |
| 11 | 2023 | 3 | |
| 12 | 2024 | 3 | |
| 13 | 2024 | 1 | |
| 14 | 2025 | 1 | |
| 15 | 2025 | 0 | |
| 16 | 2025 | 0 | |
| 17 | 2022 | 0 | |
| 18 | 2020 | 0 |
About Sungwon Ham
Sungwon Ham is a scholar working on Radiology, Nuclear Medicine and Imaging, Pulmonary and Respiratory Medicine, Artificial Intelligence, Surgery and Biomedical Engineering, having authored 18 papers that have together received 385 indexed citations. Recurring topics across this work include Radiomics and Machine Learning in Medical Imaging (7 papers), MRI in cancer diagnosis (4 papers), AI in cancer detection (3 papers), Medical Imaging Techniques and Applications (2 papers), Head and Neck Cancer Studies (2 papers), Cerebrovascular and Carotid Artery Diseases (2 papers), Glioma Diagnosis and Treatment (2 papers) and COVID-19 diagnosis using AI (1 paper). The work is most often cited by research in Health Informatics (40 citations), Radiology, Nuclear Medicine and Imaging (202 citations), Otorhinolaryngology (34 citations), Neurology (47 citations) and Health Information Management (24 citations). Sungwon Ham has collaborated with scholars based in South Korea, Japan and United States. Frequent co-authors include Namkug Kim, Hyunna Lee, Jihye Yun, Ji Eun Park, Ho Sung Kim, Han-Bin Lee, Eun‐Jae Lee, Jong S. Kim, Ji Sung Lee and Sun U. Kwon. Their work appears in journals such as Scientific Reports, Korean Journal of Radiology, European Radiology, La radiologia medica and Breast Cancer Research.
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.