Brian Hurt
Impact in
- Health Informatics top 5%
- Artificial Intelligence in Healthcare and Education
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- COVID-19 diagnosis using AI
- Radiomics and Machine Learning in Medical Imaging
Papers in
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- COVID-19 diagnosis using AI 6
- Radiomics and Machine Learning in Medical Imaging 2
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- Vascular Malformations Diagnosis and Treatment 2
- Brain Tumor Detection and Classification 1
- Co-authors
- Albert Hsiao (8 shared papers)Karim C. El Kasmi (1 shared paper)Carlton C. Barnett (1 shared paper)Barish H. Edil (1 shared paper)Seth Kligerman (4 shared papers)Richard D. Schulick (1 shared paper)Jorgé Nieva (2 shared papers)Jeremy Mason (2 shared papers)
- Journals
- Journal of Thoracic Imaging (3 papers)Scientific Reports (2 papers)Journal of Digital Imaging (1 paper)npj Breast Cancer (1 paper)The American Journal of Surgery (1 paper)
- Partner nations
- United States
In The Last Decade
Brian Hurt
11 papers receiving 303 citations
Peers
Comparison fields: 5 of 67
- Health Informatics 30
- Radiology, Nuclear Medicine and Imaging 84
- Oncology 70
- Modeling and Simulation 12
- Immunology 53
Countries citing papers authored by Brian Hurt
This map shows the geographic impact of Brian Hurt'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 Brian Hurt with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Brian Hurt more than expected).
Fields of papers citing papers by Brian Hurt
This network shows the impact of papers produced by Brian Hurt. 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 Brian Hurt. The network helps show where Brian Hurt may publish in the future.
Co-authors
The 25 scholars most cited alongside Brian Hurt, 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 | 2017 | 103 | |
| 2 | 2020 | 48 | |
| 3 | 2015 | 35 | |
| 4 | 2020 | 35 | |
| 5 | 2014 | 26 | |
| 6 | 2022 | 21 | |
| 7 | 2020 | 18 | |
| 8 | 2022 | 15 | |
| 9 | 2021 | 4 | |
| 10 | 2022 | 3 | |
| 11 | 2020 | 2 | |
| 12 | 2020 | 0 |
About Brian Hurt
Brian Hurt is a scholar working on Radiology, Nuclear Medicine and Imaging, Neurology, Pulmonary and Respiratory Medicine, Biomedical Engineering and Surgery, having authored 12 papers that have together received 310 indexed citations. Recurring topics across this work include COVID-19 diagnosis using AI (6 papers), Radiomics and Machine Learning in Medical Imaging (2 papers), Medical Imaging and Analysis (2 papers), Vascular Malformations Diagnosis and Treatment (2 papers), Lung Cancer Diagnosis and Treatment (2 papers), Cancer Genomics and Diagnostics (1 paper), Brain Tumor Detection and Classification (1 paper) and Mathematical Biology Tumor Growth (1 paper). The work is most often cited by research in Health Informatics (30 citations), Radiology, Nuclear Medicine and Imaging (84 citations), Oncology (70 citations), Modeling and Simulation (12 citations) and Immunology (53 citations). Brian Hurt has collaborated with scholars based in United States. Frequent co-authors include Albert Hsiao, Karim C. El Kasmi, Carlton C. Barnett, Barish H. Edil, Seth Kligerman, Richard D. Schulick, Jorgé Nieva, Jeremy Mason, Peter Kühn and Michael Hogarth. Their work appears in journals such as Journal of Thoracic Imaging, Scientific Reports, Journal of Digital Imaging, npj Breast Cancer and The American Journal of 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.