Riham Eiada
Impact in
- Health Informatics top 5%
- Artificial Intelligence in Healthcare and Education
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- COVID-19 diagnosis using AI
- Radiomics and Machine Learning in Medical Imaging
- MRI in cancer diagnosis
Papers in
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- MRI in cancer diagnosis 3
- Radiomics and Machine Learning in Medical Imaging 1
- COVID-19 diagnosis using AI 1
- Surgery 1
- Breast Implant and Reconstruction 1
- Reconstructive Surgery and Microvascular Techniques 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)Yuxin Huang (1 shared paper)Zhaowen Qiu (1 shared paper)Lihua Li (1 shared paper)
- Journals
- Menopause The Journal of The North American Menopause Society (1 paper)British Journal of Radiology (1 paper)Radiology (1 paper)IEEE Transactions on Medical Imaging (1 paper)American Journal of Roentgenology (1 paper)
- Partner nations
- CanadaSaudi ArabiaEgypt
In The Last Decade
Riham Eiada
7 papers receiving 355 citations
Peers
Comparison fields: 5 of 48
- Health Informatics 32
- Radiology, Nuclear Medicine and Imaging 198
- Pathology and Forensic Medicine 104
- Cancer Research 72
- Dermatology 36
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 | 130 | |
| 2 | 2012 | 92 | |
| 3 | 2011 | 83 | |
| 4 | 2012 | 23 | |
| 5 | 2021 | 18 | |
| 6 | 2012 | 9 | |
| 7 | 2010 | 3 |
About Riham Eiada
Riham Eiada is a scholar working on Radiology, Nuclear Medicine and Imaging, Surgery, Pathology and Forensic Medicine, Pulmonary and Respiratory Medicine and Oncology, having authored 7 papers that have together received 358 indexed citations. Recurring topics across this work include MRI in cancer diagnosis (3 papers), Radiomics and Machine Learning in Medical Imaging (1 paper), Artificial Intelligence in Healthcare and Education (1 paper), Breast Cancer Treatment Studies (1 paper), Cancer Immunotherapy and Biomarkers (1 paper), Breast Implant and Reconstruction (1 paper), Reconstructive Surgery and Microvascular Techniques (1 paper) and COVID-19 diagnosis using AI (1 paper). The work is most often cited by research in Health Informatics (32 citations), Radiology, Nuclear Medicine and Imaging (198 citations), Pathology and Forensic Medicine (104 citations), Cancer Research (72 citations) and Dermatology (36 citations). Riham Eiada has collaborated with scholars based in Canada, Saudi Arabia and Egypt. Frequent co-authors include Supriya Kulkarni, Pavel Crystal, Anabel M. Scaranelo, Derek Muradali, Lindsay M. Jacks, Yuxin Huang, Zhaowen Qiu, Lihua Li, Shahrukh K. Hashmi and Longxi Zhou. Their work appears in journals such as Menopause The Journal of The North American Menopause Society, British Journal of Radiology, Radiology, IEEE Transactions on Medical Imaging and American Journal of Roentgenology.
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.