Kwang-Nam Jin
- Radiology, Nuclear Medicine and Imaging top 2%
- Pulmonary and Respiratory Medicine top 5%
- Artificial Intelligence top 10%
- Health Informatics top 0.5%
- Biomedical Engineering
- Co-authors
- Sunggyun ParkJong Hyuk LeeChang Min ParkEui Jin HwangJin Mo GooJae Ho SohnJu Gang NamSangheum Hwang
- Topics
- Lung Cancer Diagnosis and Treatment (5 papers)COVID-19 diagnosis using AI (4 papers)Radiomics and Machine Learning in Medical Imaging (3 papers)
- Cited by
- Health InformaticsRadiology, Nuclear Medicine and ImagingPulmonary and Respiratory Medicine
- Journals
- American Journal of Respiratory and Critical Care MedicineClinical Infectious DiseasesRadiology
- Partner nations
- South KoreaUnited StatesEthiopia
In The Last Decade
Kwang-Nam Jin
10 papers receiving 948 citations
Hit Papers
Peers
Comparison fields: 5 of 83
- Radiology, Nuclear Medicine and Imaging 734
- Pulmonary and Respiratory Medicine 430
- Artificial Intelligence 199
- Health Informatics 181
- Biomedical Engineering 79
Countries citing papers authored by Kwang-Nam Jin
This map shows the geographic impact of Kwang-Nam Jin'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 Kwang-Nam Jin with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Kwang-Nam Jin more than expected).
Fields of papers citing papers by Kwang-Nam Jin
This network shows the impact of papers produced by Kwang-Nam Jin. 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 Kwang-Nam Jin. The network helps show where Kwang-Nam Jin may publish in the future.
Co-authorship network of co-authors of Kwang-Nam Jin
This figure shows the co-authorship network connecting the top 25 collaborators of Kwang-Nam Jin. A scholar is included among the top collaborators of Kwang-Nam Jin based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Kwang-Nam Jin. Kwang-Nam Jin is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 69 | |
| 2 | Development and Validation of a Deep Learning–Based Automated Detection Algorithm for Major Thoracic Diseases on Chest Radiographsbreakdown → | 289 |
| 3 | Development and Validation of Deep Learning–based Automatic Detection Algorithm for Malignant Pulmonary Nodules on Chest Radiographsbreakdown → | 382 |
| 4 | 162 | |
| 5 | 3 | |
| 6 | 16 | |
| 7 | 7 | |
| 8 | 7 | |
| 9 | 1 | |
| 10 | 35 |
About Kwang-Nam Jin
Kwang-Nam Jin is a scholar working on Radiology, Nuclear Medicine and Imaging, Pulmonary and Respiratory Medicine and Parasitology, having authored 10 papers that have together received 971 indexed citations. Recurring topics across this work include Lung Cancer Diagnosis and Treatment (5 papers), COVID-19 diagnosis using AI (4 papers) and Radiomics and Machine Learning in Medical Imaging (3 papers). The work is most often cited by research in Health Informatics (181 citations), Radiology, Nuclear Medicine and Imaging (734 citations) and Pulmonary and Respiratory Medicine (430 citations). Kwang-Nam Jin has collaborated with scholars based in South Korea, United States and Ethiopia. Frequent co-authors include Sunggyun Park, Jong Hyuk Lee, Chang Min Park, Eui Jin Hwang, Jin Mo Goo, Jae Ho Sohn, Ju Gang Nam, Sangheum Hwang, Kun Young Lim and Jung Im Kim. Their work appears in journals such as American Journal of Respiratory and Critical Care Medicine, Clinical Infectious Diseases and 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.