Connie Kim
Impact in
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- MRI in cancer diagnosis
- Radiomics and Machine Learning in Medical Imaging
- Advanced MRI Techniques and Applications
- Medical Imaging Techniques and Applications
- Emergency Medicine top 10%
Papers in
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- MRI in cancer diagnosis 4
- Radiomics and Machine Learning in Medical Imaging 4
- Medical Imaging Techniques and Applications 2
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- AI in cancer detection 6
- Co-authors
- Sora C. Yoon (8 shared papers)Mary Scott Soo (8 shared papers)Lars J. Grimm (7 shared papers)Karen S. Johnson (4 shared papers)Sujata V. Ghate (1 shared paper)Sujata V. Ghate (8 shared papers)Maciej A. Mazurowski (4 shared papers)Michael P. Federle (1 shared paper)
- Journals
- Academic Radiology (5 papers)Medical Physics (3 papers)American Journal of Roentgenology (2 papers)European Journal of Radiology (1 paper)Journal of the American College of Radiology (1 paper)
- Partner nations
- United StatesChinaSouth Korea
In The Last Decade
Connie Kim
13 papers receiving 455 citations
Peers
Comparison fields: 5 of 65
- Radiology, Nuclear Medicine and Imaging 242
- Emergency Medicine 49
- Urology 29
- Pathology and Forensic Medicine 55
- Cancer Research 38
Countries citing papers authored by Connie Kim
This map shows the geographic impact of Connie 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 Connie Kim with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Connie Kim more than expected).
Fields of papers citing papers by Connie Kim
This network shows the impact of papers produced by Connie 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 Connie Kim. The network helps show where Connie Kim may publish in the future.
Co-authors
The 25 scholars most cited alongside Connie Kim, 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 | 2015 | 121 | |
| 2 | 2002 | 103 | |
| 3 | 2016 | 87 | |
| 4 | 2018 | 39 | |
| 5 | 2015 | 27 | |
| 6 | 2016 | 24 | |
| 7 | 2011 | 17 | |
| 8 | 2009 | 10 | |
| 9 | 2014 | 9 | |
| 10 | 2020 | 7 | |
| 11 | 2019 | 6 | |
| 12 | 2020 | 6 | |
| 13 | 2016 | 4 |
About Connie Kim
Connie Kim is a scholar working on Radiology, Nuclear Medicine and Imaging, Artificial Intelligence, Pathology and Forensic Medicine, Pulmonary and Respiratory Medicine and Cancer Research, having authored 13 papers that have together received 460 indexed citations. Recurring topics across this work include AI in cancer detection (6 papers), Digital Radiography and Breast Imaging (4 papers), MRI in cancer diagnosis (4 papers), Radiomics and Machine Learning in Medical Imaging (4 papers), Breast Lesions and Carcinomas (4 papers), Breast Cancer Treatment Studies (3 papers), Medical Imaging Techniques and Applications (2 papers) and Global Cancer Incidence and Screening (2 papers). The work is most often cited by research in Radiology, Nuclear Medicine and Imaging (242 citations), Emergency Medicine (49 citations), Urology (29 citations), Pathology and Forensic Medicine (55 citations) and Cancer Research (38 citations). Connie Kim has collaborated with scholars based in United States, China and South Korea. Frequent co-authors include Sora C. Yoon, Mary Scott Soo, Lars J. Grimm, Karen S. Johnson, Sujata V. Ghate, Sujata V. Ghate, Maciej A. Mazurowski, Michael P. Federle, R. Brooke Jeffrey and Michael J. Lane. Their work appears in journals such as Academic Radiology, Medical Physics, American Journal of Roentgenology, European Journal of Radiology and Journal of the American College of Radiology.
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.