Jared Dunnmon
Impact in
- Health Informatics top 1%
- Artificial Intelligence in Healthcare and Education
- Internal Medicine top 5%
- Venous Thromboembolism Diagnosis and Management
Papers in
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- Machine Learning in Healthcare 3
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- Radiomics and Machine Learning in Medical Imaging 5
- COVID-19 diagnosis using AI 3
- Radiology practices and education 2
- Co-authors
- Christopher Ré (11 shared papers)Earl H. Dowell (1 shared paper)Brian P. Mann (1 shared paper)Samuel C. Stanton (1 shared paper)Daniel L. Rubin (7 shared papers)Matthew P. Lungren (6 shared papers)Curtis P. Langlotz (2 shared papers)Darvin Yi (1 shared paper)
- Journals
- npj Digital Medicine (2 papers)Nature Communications (2 papers)Experiments in Fluids (1 paper)Journal of Fluids and Structures (1 paper)Radiology (1 paper)
- Partner nations
- United StatesCanadaGermany
In The Last Decade
Jared Dunnmon
26 papers receiving 1.0k citations
Peers
Comparison fields: 5 of 108
- Health Informatics 94
- Internal Medicine 69
- Radiology, Nuclear Medicine and Imaging 267
- Artificial Intelligence 293
- Computational Mechanics 149
Countries citing papers authored by Jared Dunnmon
This map shows the geographic impact of Jared Dunnmon'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 Jared Dunnmon with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Jared Dunnmon more than expected).
Fields of papers citing papers by Jared Dunnmon
This network shows the impact of papers produced by Jared Dunnmon. 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 Jared Dunnmon. The network helps show where Jared Dunnmon may publish in the future.
Co-authors
The 25 scholars most cited alongside Jared Dunnmon, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
Showing the 20 most-cited of 26 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | 2011 | 194 | |
| 2 | 2018 | 156 | |
| 3 | 2020 | 117 | |
| 4 | 2019 | 84 | |
| 5 | 2019 | 61 | |
| 6 | 2019 | 56 | |
| 7 | 2020 | 53 | |
| 8 | 2016 | 49 | |
| 9 | Learning to Compose Domain-Specific Transformations for Data Augmentation. | 2017 | 36 |
| 10 | 2021 | 27 | |
| 11 | 2018 | 24 | |
| 12 | 2020 | 22 | |
| 13 | 2022 | 22 | |
| 14 | 2021 | 19 | |
| 15 | 2023 | 19 | |
| 16 | 2017 | 14 | |
| 17 | 2020 | 12 | |
| 18 | 2019 | 11 | |
| 19 | 2019 | 10 | |
| 20 | 2021 | 9 |
About Jared Dunnmon
Jared Dunnmon is a scholar working on Artificial Intelligence, Radiology, Nuclear Medicine and Imaging, Health Informatics, Pediatrics, Perinatology and Child Health and Pulmonary and Respiratory Medicine, having authored 26 papers that have together received 1.0k indexed citations. Recurring topics across this work include Radiomics and Machine Learning in Medical Imaging (5 papers), Artificial Intelligence in Healthcare and Education (5 papers), Machine Learning in Healthcare (3 papers), COVID-19 diagnosis using AI (3 papers), Venous Thromboembolism Diagnosis and Management (2 papers), Radiative Heat Transfer Studies (2 papers), Radiology practices and education (2 papers) and Neonatal and fetal brain pathology (2 papers). The work is most often cited by research in Health Informatics (94 citations), Internal Medicine (69 citations), Radiology, Nuclear Medicine and Imaging (267 citations), Artificial Intelligence (293 citations) and Computational Mechanics (149 citations). Jared Dunnmon has collaborated with scholars based in United States, Canada and Germany. Frequent co-authors include Christopher Ré, Earl H. Dowell, Brian P. Mann, Samuel C. Stanton, Daniel L. Rubin, Matthew P. Lungren, Curtis P. Langlotz, Darvin Yi, Alexander Ratner and Bhavik N. Patel. Their work appears in journals such as npj Digital Medicine, Nature Communications, Experiments in Fluids, Journal of Fluids and Structures 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.