Nathaniel R. Greenbaum
- Artificial Intelligence top 2%
- Radiology, Nuclear Medicine and Imaging top 5%
- Computer Vision and Pattern Recognition top 5%
- Health Informatics top 0.5%
- Pulmonary and Respiratory Medicine
- Co-authors
- Steven HorngChih-Ying DengMatthew P. LungrenAlistair E. W. JohnsonTom PollardSeth J. BerkowitzRoger G. MarkLarry Nathanson
- Topics
- Lung Cancer Diagnosis and Treatment (2 papers)Nursing Diagnosis and Documentation (2 papers)COVID-19 diagnosis using AI (2 papers)
- Journals
- Journal of the American College of CardiologyScientific DataInternational Journal of Medical Informatics
- Partner nations
- United StatesIsrael
In The Last Decade
Nathaniel R. Greenbaum
6 papers receiving 808 citations
Hit Papers
Peers
Comparison fields: 5 of 68
- Artificial Intelligence 504
- Radiology, Nuclear Medicine and Imaging 466
- Computer Vision and Pattern Recognition 188
- Health Informatics 172
- Pulmonary and Respiratory Medicine 119
Countries citing papers authored by Nathaniel R. Greenbaum
This map shows the geographic impact of Nathaniel R. Greenbaum'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 Nathaniel R. Greenbaum with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Nathaniel R. Greenbaum more than expected).
Fields of papers citing papers by Nathaniel R. Greenbaum
This network shows the impact of papers produced by Nathaniel R. Greenbaum. 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 Nathaniel R. Greenbaum. The network helps show where Nathaniel R. Greenbaum may publish in the future.
Co-authorship network of co-authors of Nathaniel R. Greenbaum
This figure shows the co-authorship network connecting the top 25 collaborators of Nathaniel R. Greenbaum. A scholar is included among the top collaborators of Nathaniel R. Greenbaum based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Nathaniel R. Greenbaum. Nathaniel R. Greenbaum is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 2 | |
| 2 | 0 | |
| 3 | 1 | |
| 4 | 25 | |
| 5 | MIMIC-CXR, a de-identified publicly available database of chest radiographs with free-text reportsbreakdown → | 773 |
| 6 | 12 | |
| 7 | 8 |
About Nathaniel R. Greenbaum
Nathaniel R. Greenbaum is a scholar working on Issues, ethics and legal aspects, Health Information Management and Emergency Medicine, having authored 7 papers that have together received 821 indexed citations. Recurring topics across this work include Lung Cancer Diagnosis and Treatment (2 papers), Nursing Diagnosis and Documentation (2 papers) and COVID-19 diagnosis using AI (2 papers). The work is most often cited by research in Health Informatics (172 citations), Radiology, Nuclear Medicine and Imaging (466 citations) and Artificial Intelligence (504 citations). Nathaniel R. Greenbaum has collaborated with scholars based in United States and Israel. Frequent co-authors include Steven Horng, Chih-Ying Deng, Matthew P. Lungren, Alistair E. W. Johnson, Tom Pollard, Seth J. Berkowitz, Roger G. Mark, Larry Nathanson, David Sontag and Yoni Halpern. Their work appears in journals such as Journal of the American College of Cardiology, Scientific Data and International Journal of Medical Informatics.
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.