Riham Eiada
Impact in
- Health Informatics top 5%
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- Radiomics and Machine Learning in Medical Imaging
- COVID-19 diagnosis using AI
- MRI in cancer diagnosis
- Medical Imaging Techniques and Applications
Papers in
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- Breast Lesions and Carcinomas 4
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- Radiomics and Machine Learning in Medical Imaging 3
- MRI in cancer diagnosis 3
- Medical Imaging Techniques and Applications 1
- Co-authors
- Supriya Kulkarni (2 shared papers)Pavel Crystal (3 shared papers)Anabel M. Scaranelo (3 shared papers)Derek Muradali (1 shared paper)Lindsay M. Jacks (1 shared paper)Longxi Zhou (1 shared paper)Zhongxiao Li (1 shared paper)Yuxin Huang (1 shared paper)
- Journals
- IEEE Transactions on Medical Imaging (1 paper)Scientific Reports (1 paper)Radiology (1 paper)Menopause The Journal of The North American Menopause Society (1 paper)British Journal of Radiology (1 paper)
- Partner nations
- CanadaSaudi ArabiaIsrael
In The Last Decade
Riham Eiada
7 papers receiving 352 citations
Peers
Comparison fields: 5 of 51
- Health Informatics 34
- Radiology, Nuclear Medicine and Imaging 243
- Pathology and Forensic Medicine 141
- Cancer Research 104
- Dermatology 45
Countries citing papers authored by Riham Eiada
This map shows the geographic impact of Riham Eiada'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 Riham Eiada with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Riham Eiada more than expected).
Fields of papers citing papers by Riham Eiada
This network shows the impact of papers produced by Riham Eiada. 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 Riham Eiada. The network helps show where Riham Eiada may publish in the future.
Co-authors
The 25 scholars most cited alongside Riham Eiada, 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 | 2020 | 129 | |
| 2 | 2012 | 91 | |
| 3 | 2011 | 82 | |
| 4 | 2012 | 23 | |
| 5 | 2021 | 18 | |
| 6 | 2012 | 9 | |
| 7 | 2010 | 3 |
About Riham Eiada
Riham Eiada is a scholar working on Pathology and Forensic Medicine, Radiology, Nuclear Medicine and Imaging, Pulmonary and Respiratory Medicine, Surgery and Oncology, having authored 7 papers that have together received 355 indexed citations. Recurring topics across this work include Breast Lesions and Carcinomas (4 papers), Radiomics and Machine Learning in Medical Imaging (3 papers), MRI in cancer diagnosis (3 papers), CAR-T cell therapy research (1 paper), Brain Metastases and Treatment (1 paper), AI in cancer detection (1 paper), Breast Cancer Treatment Studies (1 paper) and Medical Imaging Techniques and Applications (1 paper). The work is most often cited by research in Health Informatics (34 citations), Radiology, Nuclear Medicine and Imaging (243 citations), Pathology and Forensic Medicine (141 citations), Cancer Research (104 citations) and Dermatology (45 citations). Riham Eiada has collaborated with scholars based in Canada, Saudi Arabia and Israel. Frequent co-authors include Supriya Kulkarni, Pavel Crystal, Anabel M. Scaranelo, Derek Muradali, Lindsay M. Jacks, Longxi Zhou, Zhongxiao Li, Yuxin Huang, Juexiao Zhou and Xigang Xiao. Their work appears in journals such as IEEE Transactions on Medical Imaging, Scientific Reports, Radiology, Menopause The Journal of The North American Menopause Society and British Journal of 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.