Peter Filev
Impact in
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- Ultrasound in Clinical Applications
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- COVID-19 diagnosis using AI
- Cardiac Imaging and Diagnostics
- Radiomics and Machine Learning in Medical Imaging
- Advanced MRI Techniques and Applications
Papers in
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- COVID-19 diagnosis using AI 5
- Radiomics and Machine Learning in Medical Imaging 3
- Cardiac Imaging and Diagnostics 2
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- AI in cancer detection 3
- Co-authors
- Constantine A. Raptis (3 shared papers)Seth Kligerman (3 shared papers)Travis S. Henry (3 shared papers)Andrew J. Bierhals (2 shared papers)Mark M. Hammer (2 shared papers)Sanjeev Bhalla (2 shared papers)Michael D. Hope (2 shared papers)Jean Jeudy (2 shared papers)
- Journals
- Medical Physics (2 papers)JACC. Cardiovascular imaging (1 paper)American Journal of Roentgenology (1 paper)CHEST Journal (1 paper)British Journal of Radiology (1 paper)
- Partner nations
- United StatesItaly
In The Last Decade
Peter Filev
14 papers receiving 317 citations
Peers
Comparison fields: 5 of 52
- Critical Care and Intensive Care Medicine 32
- Radiology, Nuclear Medicine and Imaging 145
- Infectious Diseases 76
- Health Informatics 4
- Oncology 41
Countries citing papers authored by Peter Filev
This map shows the geographic impact of Peter Filev'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 Peter Filev with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Peter Filev more than expected).
Fields of papers citing papers by Peter Filev
This network shows the impact of papers produced by Peter Filev. 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 Peter Filev. The network helps show where Peter Filev may publish in the future.
Co-authors
The 25 scholars most cited alongside Peter Filev, 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 | 2020 | 137 | |
| 2 | 2017 | 51 | |
| 3 | 2005 | 35 | |
| 4 | 2021 | 33 | |
| 5 | 2015 | 19 | |
| 6 | 2020 | 14 | |
| 7 | 2008 | 10 | |
| 8 | 2020 | 10 | |
| 9 | 2008 | 5 | |
| 10 | 2022 | 4 | |
| 11 | 2020 | 2 | |
| 12 | 2019 | 1 | |
| 13 | 2024 | 1 | |
| 14 | 2022 | 1 | |
| 15 | 2024 | 0 | |
| 16 | 2023 | 0 |
About Peter Filev
Peter Filev is a scholar working on Radiology, Nuclear Medicine and Imaging, Artificial Intelligence, Pulmonary and Respiratory Medicine, Oncology and Biomedical Engineering, having authored 16 papers that have together received 323 indexed citations. Recurring topics across this work include COVID-19 diagnosis using AI (5 papers), Radiomics and Machine Learning in Medical Imaging (3 papers), AI in cancer detection (3 papers), Cardiac Imaging and Diagnostics (2 papers), Advanced X-ray and CT Imaging (2 papers), Image Retrieval and Classification Techniques (2 papers), Colorectal Cancer Screening and Detection (2 papers) and Ultrasound and Hyperthermia Applications (1 paper). The work is most often cited by research in Critical Care and Intensive Care Medicine (32 citations), Radiology, Nuclear Medicine and Imaging (145 citations), Infectious Diseases (76 citations), Health Informatics (4 citations) and Oncology (41 citations). Peter Filev has collaborated with scholars based in United States and Italy. Frequent co-authors include Constantine A. Raptis, Seth Kligerman, Travis S. Henry, Andrew J. Bierhals, Mark M. Hammer, Sanjeev Bhalla, Michael D. Hope, Jean Jeudy, Amar Shah and Ryan G. Short. Their work appears in journals such as Medical Physics, JACC. Cardiovascular imaging, American Journal of Roentgenology, CHEST Journal and British Journal 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.