Jin‐Haeng Chung

1.9k citations
47 papers · 1.4k indexed · h-index 20
Topics
Lung Cancer Treatments and Mutations (15 papers)Radiomics and Machine Learning in Medical Imaging (9 papers)Lung Cancer Diagnosis and Treatment (8 papers)

In The Last Decade

Jin‐Haeng Chung

45 papers receiving 1.4k citations

Peers

Jin‐Haeng Chung
Comparison fields: 5 of 82
  • Pulmonary and Respiratory Medicine 891
  • Oncology 580
  • Molecular Biology 231
  • Rheumatology 202
  • Cancer Research 193
Replace Annikka Weissferdt with:
Annikka Weissferdt United States
Sarah Chiang United States
Ilhan Lim South Korea
Idris Tolgay Ocal United States
Angelo Bolognesi Italy
Daniel R. Zwahlen Switzerland
Nabil Adra United States
Anna Caliò Italy
Huaiyin Shi China
Iwona Ługowska Poland
Jin‐Haeng Chung relative to Annikka Weissferdt United States Annikka Weissferdt's profile →
Citations per field
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Annikka Weissferdt · 1×
Citations per year

Countries citing papers authored by Jin‐Haeng Chung

Since Specialization
Citations

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

Fields of papers citing papers by Jin‐Haeng Chung

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Jin‐Haeng Chung. 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 Jin‐Haeng Chung. The network helps show where Jin‐Haeng Chung may publish in the future.

Co-authorship network of co-authors of Jin‐Haeng Chung

This figure shows the co-authorship network connecting the top 25 collaborators of Jin‐Haeng Chung. A scholar is included among the top collaborators of Jin‐Haeng Chung 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 Jin‐Haeng Chung. Jin‐Haeng Chung is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
#WorkIndexed citations
1 2
2 2
3 10
4 23
5 12
6 13
7 32
8 97
9 8
10 3
11 133
12 113
13 6
14 7
15 122
16 2
17 4
18 27
19 20
20 19

About Jin‐Haeng Chung

Jin‐Haeng Chung is a scholar working on Pulmonary and Respiratory Medicine, Oncology and Rheumatology, having authored 47 papers that have together received 1.4k indexed citations. Recurring topics across this work include Lung Cancer Treatments and Mutations (15 papers), Radiomics and Machine Learning in Medical Imaging (9 papers) and Lung Cancer Diagnosis and Treatment (8 papers). The work is most often cited by research in Pulmonary and Respiratory Medicine (891 citations), Oncology (580 citations) and Rheumatology (202 citations). Jin‐Haeng Chung has collaborated with scholars based in South Korea, Ethiopia and United States. Frequent co-authors include Sanghoon Jheon, Choon‐Taek Lee, Jae‐Ho Lee, Kyung Won Lee, Tae Jung Kim, Jong Seok Lee, Byoung Chul Cho, Hyojin Kim, Hyo Sup Shim and Hye Ryun Kim. Their work appears in journals such as Journal of Clinical Oncology, Cancer and Journal of Bone and Joint Surgery.

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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