Charles E. Kahn

9.2k citations
218 papers · 4.4k · h-index 32

Impact in

Papers in

Charles E. Kahn

205 papers receiving 4.3k citations

Peers

Charles E. Kahn
Comparison fields: 5 of 186
  • Health Informatics 507
  • Radiology, Nuclear Medicine and Imaging 1.5k
  • Health Information Management 239
  • Family Practice 62
  • Artificial Intelligence 1.1k
Replace Eliot L. Siegel with:
Eliot L. Siegel United States
Yi Dong China
Georgia D. Tourassi United States
Marc Coram United States
Eric K. Oermann United States
Safwan S. Halabi United States
Benjamin S. Glicksberg United States
Yilong Wang China
Curtis P. Langlotz United States
Francesco Sardanelli Italy
Charles E. Kahn relative to Eliot L. Siegel United States Eliot L. Siegel's profile →
Citations per field
00.5×1.7×
Eliot L. Siegel · 1×
Citations per year

Countries citing papers authored by Charles E. Kahn

Since Specialization
Citations

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

Fields of papers citing papers by Charles E. Kahn

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Charles E. Kahn. 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 Charles E. Kahn. The network helps show where Charles E. Kahn may publish in the future.

Co-authors

The 25 scholars most cited alongside Charles E. Kahn, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Charles E. Kahn Line = papers co-authored together Charles E. Kahn links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 218 papers — load more, or switch the sort, to bring in the rest.

#Work
1 2022188
2 2009181
3 1998171
4 2009145
5 1997139
6 1993125
7 1986125
8 2021122
9 2010117
10 201099
11 199482
12 200774
13 200973
14 200873
15 199472
16 201372
17 201472
18 201670
19
polycystic kidney disease
199565
20 200965

About Charles E. Kahn

Charles E. Kahn is a scholar working on Radiology, Nuclear Medicine and Imaging, Artificial Intelligence, Molecular Biology, Health Informatics and Pulmonary and Respiratory Medicine, having authored 218 papers that have together received 4.4k indexed citations. Recurring topics across this work include Radiology practices and education (52 papers), Biomedical Text Mining and Ontologies (48 papers), Radiomics and Machine Learning in Medical Imaging (29 papers), Artificial Intelligence in Healthcare and Education (26 papers), AI in cancer detection (22 papers), Semantic Web and Ontologies (16 papers), Topic Modeling (15 papers) and Radiation Dose and Imaging (13 papers). The work is most often cited by research in Health Informatics (507 citations), Radiology, Nuclear Medicine and Imaging (1.5k citations), Health Information Management (239 citations), Family Practice (62 citations) and Artificial Intelligence (1.1k citations). Charles E. Kahn has collaborated with scholars based in United States, Switzerland and South Korea. Frequent co-authors include Daniel L. Rubin, Elizabeth S. Burnside, Peter Haddawy, Tessa S. Cook, Jagpreet Chhatwal, Oğuzhan Alagöz, Katherine A. Shaffer, Henning Müller, John A. Carrino and Turgay Ayer. Their work appears in journals such as Journal of Digital Imaging, Journal of the American College of Radiology, Radiographics, Radiology Artificial Intelligence 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.

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