Chris Chute
Impact in
- Health Informatics top 0.5%
- 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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- Radiomics and Machine Learning in Medical Imaging 1
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- Venous Thromboembolism Diagnosis and Management 2
- Co-authors
- Bhavik N. Patel (4 shared papers)Pranav Rajpurkar (4 shared papers)Andrew Y. Ng (4 shared papers)Matthew P. Lungren (4 shared papers)Jeremy Irvin (4 shared papers)Curtis P. Langlotz (2 shared papers)Robyn L. Ball (3 shared papers)Katie Shpanskaya (2 shared papers)
- Journals
- npj Digital Medicine (1 paper)Scientific Reports (1 paper)PubMed (1 paper)Zenodo (CERN European Organization for Nuclear Research) (1 paper)SSRN Electronic Journal (1 paper)
- Partner nations
- United StatesCanada
In The Last Decade
Chris Chute
6 papers receiving 1.7k citations
Chris Chute's Hit Papers
Peers
Comparison fields: 5 of 93
- Health Informatics 230
- Radiology, Nuclear Medicine and Imaging 1.1k
- Artificial Intelligence 969
- Internal Medicine 69
- Computer Vision and Pattern Recognition 360
Countries citing papers authored by Chris Chute
This map shows the geographic impact of Chris Chute'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 Chris Chute with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Chris Chute more than expected).
Fields of papers citing papers by Chris Chute
This network shows the impact of papers produced by Chris Chute. 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 Chris Chute. The network helps show where Chris Chute may publish in the future.
Co-authors
The 25 scholars most cited alongside Chris Chute, 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 | CheXpert: A Large Chest Radiograph Dataset with Uncertainty Labels and Expert Comparison Hit paper breakdown → | 2019 | 1547 |
| 2 | 2020 | 117 | |
| 3 | 2020 | 83 | |
| 4 | 2019 | 10 | |
| 5 | LexValueSets: an approach for context-driven value sets extraction. | 2008 | 8 |
| 6 | 2020 | 8 |
About Chris Chute
Chris Chute is a scholar working on Radiology, Nuclear Medicine and Imaging, Internal Medicine, Artificial Intelligence, Molecular Biology and Pulmonary and Respiratory Medicine, having authored 6 papers that have together received 1.8k indexed citations. Recurring topics across this work include Venous Thromboembolism Diagnosis and Management (2 papers), Radiomics and Machine Learning in Medical Imaging (1 paper), Machine Learning in Healthcare (1 paper), Biomedical Text Mining and Ontologies (1 paper), Advanced X-ray and CT Imaging (1 paper), Semantic Web and Ontologies (1 paper), linguistics and terminology studies (1 paper) and Health, Environment, Cognitive Aging (1 paper). The work is most often cited by research in Health Informatics (230 citations), Radiology, Nuclear Medicine and Imaging (1.1k citations), Artificial Intelligence (969 citations), Internal Medicine (69 citations) and Computer Vision and Pattern Recognition (360 citations). Chris Chute has collaborated with scholars based in United States and Canada. Frequent co-authors include Bhavik N. Patel, Pranav Rajpurkar, Andrew Y. Ng, Matthew P. Lungren, Jeremy Irvin, Curtis P. Langlotz, Robyn L. Ball, Katie Shpanskaya, Jayne Seekins and Safwan S. Halabi. Their work appears in journals such as npj Digital Medicine, Scientific Reports, PubMed, Zenodo (CERN European Organization for Nuclear Research) and SSRN Electronic Journal.
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.