David Gur
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- Radiology practices and education 42
- Radiomics and Machine Learning in Medical Imaging 41
- Medical Imaging Techniques and Applications 35
- Radiation Dose and Imaging 29
- Pulmonary and Respiratory Medicine top 0.2%
- Digital Radiography and Breast Imaging 71
- Lung Cancer Diagnosis and Treatment 25
- Artificial Intelligence top 0.2%
- AI in cancer detection 113
- Neurology top 1%
- Oncology top 2%
- Global Cancer Incidence and Screening 37
David Gur
322 papers receiving 8.3k citations
Hit Papers
Peers
Comparison fields: 5 of 178
- Radiology, Nuclear Medicine and Imaging 4.0k
- Pulmonary and Respiratory Medicine 4.2k
- Artificial Intelligence 3.5k
- Neurology 859
- Oncology 1.4k
Countries citing papers authored by David Gur
This map shows the geographic impact of David Gur'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 David Gur with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites David Gur more than expected).
Fields of papers citing papers by David Gur
This network shows the impact of papers produced by David Gur. 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 David Gur. The network helps show where David Gur may publish in the future.
Co-authorship network
The 25 scholars most cited alongside David Gur, 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 | 2019 | 116 | |
| 2 | 2019 | 67 | |
| 3 | 2015 | 17 | |
| 4 | 2014 | 16 | |
| 5 | 2013 | 11 | |
| 6 | 2012 | 128 | |
| 7 | 2010 | 17 | |
| 8 | 2010 | 95 | |
| 9 | 2010 | 22 | |
| 10 | 2008 | 96 | |
| 11 | 2007 | 14 | |
| 12 | 2006 | 10 | |
| 13 | 2002 | 25 | |
| 14 | 2001 | 24 | |
| 15 | 2000 | 3 | |
| 16 | 2000 | 14 | |
| 17 | 1999 | 2 | |
| 18 | 1984 | 53 | |
| 19 | Xenon- and Iodine-Enhanced CT of Diffuse Cerebral Circulatory Arrest | 1980 | 2 |
| 20 | Dynamic computed tomography of the lung: regional ventilation measurements. | 1979 | 62 |
About David Gur
David Gur is a scholar working on Radiology, Nuclear Medicine and Imaging, Developmental Neuroscience and Artificial Intelligence, having authored 330 papers that have together received 8.6k indexed citations. Recurring topics across this work include AI in cancer detection (113 papers), Digital Radiography and Breast Imaging (71 papers), Radiology practices and education (42 papers), Radiomics and Machine Learning in Medical Imaging (41 papers), Global Cancer Incidence and Screening (37 papers), Medical Imaging Techniques and Applications (35 papers), Radiation Dose and Imaging (29 papers) and Lung Cancer Diagnosis and Treatment (25 papers). The work is most often cited by research in Radiology, Nuclear Medicine and Imaging (4.0k citations), Pulmonary and Respiratory Medicine (4.2k citations) and Artificial Intelligence (3.5k citations). David Gur has collaborated with scholars based in United States, Israel and China. Frequent co-authors include Bin Zheng, Andriy I. Bandos, Howard E. Rockette, Jules H. Sumkin, Marie A. Ganott, Christiane M. Hakim, Walter F. Good, Howard Yonas, Margarita L. Zuley and Denise M. Chough. Their work appears in journals such as Academic Radiology, Medical Physics, Radiology, American Journal of Roentgenology and Investigative 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.