Rima Arnaout
- Health Informatics top 0.2%
- Artificial Intelligence in Healthcare and Education 10
-
- Cardiovascular Function and Risk Factors 5
- Cardiomyopathy and Myosin Studies 4
- Cell Biology top 5%
-
- Cardiac Imaging and Diagnostics 5
- Radiomics and Machine Learning in Medical Imaging 4
-
- Congenital heart defects research 9
-
- Congenital Heart Disease Studies 7
-
- Machine Learning in Healthcare 4
- Co-authors
- Ramy ArnaoutDidier Y. R. StainierAli MadaniSven ReischauerMohammad R. K. MofradGiorgio QuerPhilipp GutMartin Tristani‐Firouzi
- Journals
- Proceedings of the National Academy of Sciences (1 paper)Circulation (3 papers)Nature Medicine (4 papers)
- Partner nations
- United StatesGermanyUnited Kingdom
In The Last Decade
Rima Arnaout
39 papers receiving 2.0k citations
Hit Papers
Peers
Comparison fields: 5 of 154
- Health Informatics 328
- Cardiology and Cardiovascular Medicine 705
- Health Information Management 102
- Cell Biology 335
- Radiology, Nuclear Medicine and Imaging 461
Countries citing papers authored by Rima Arnaout
This map shows the geographic impact of Rima Arnaout'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 Rima Arnaout with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Rima Arnaout more than expected).
Fields of papers citing papers by Rima Arnaout
This network shows the impact of papers produced by Rima Arnaout. 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 Rima Arnaout. The network helps show where Rima Arnaout may publish in the future.
Co-authorship network
The 25 scholars most cited alongside Rima Arnaout, 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 | 2025 | 1 | |
| 2 | 2025 | 1 | |
| 3 | 2025 | 9 | |
| 4 | 2025 | 2 | |
| 5 | 2025 | 1 | |
| 6 | 2024 | 2 | |
| 7 | 2024 | 15 | |
| 8 | 2023 | 10 | |
| 9 | 2023 | 16 | |
| 10 | 2023 | 15 | |
| 11 | 2023 | 2 | |
| 12 | 2023 | 1 | |
| 13 | 2023 | 21 | |
| 14 | 2021 | 13 | |
| 15 | Abstract 14334: Postpartum Depression: A Novel Predictor of Cardiovascular Disease Risk in Women | 2018 | 5 |
| 16 | 2016 | 97 | |
| 17 | 2014 | 53 | |
| 18 | 2014 | 10 | |
| 19 | 2011 | 2 | |
| 20 | 2008 | 207 |
About Rima Arnaout
Rima Arnaout is a scholar working on Health Informatics, Cardiology and Cardiovascular Medicine and Radiology, Nuclear Medicine and Imaging, having authored 41 papers that have together received 2.1k indexed citations. Recurring topics across this work include Artificial Intelligence in Healthcare and Education (10 papers), Congenital heart defects research (9 papers), Congenital Heart Disease Studies (7 papers), Cardiac Imaging and Diagnostics (5 papers), Cardiovascular Function and Risk Factors (5 papers), Cardiomyopathy and Myosin Studies (4 papers), Radiomics and Machine Learning in Medical Imaging (4 papers) and Machine Learning in Healthcare (4 papers). The work is most often cited by research in Health Informatics (328 citations), Cardiology and Cardiovascular Medicine (705 citations) and Health Information Management (102 citations). Rima Arnaout has collaborated with scholars based in United States, Germany and United Kingdom. Frequent co-authors include Ramy Arnaout, Didier Y. R. Stainier, Ali Madani, Sven Reischauer, Mohammad R. K. Mofrad, Giorgio Quer, Philipp Gut, Martin Tristani‐Firouzi, Jan Huisken and Tania Ferrer. Their work appears in journals such as Proceedings of the National Academy of Sciences, Circulation and Nature Medicine.
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.