Farida Mohsen
Impact in
- Health Informatics top 2%
- Artificial Intelligence in Healthcare and Education
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- Artificial Intelligence in Healthcare
Papers in
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- Machine Learning in Healthcare 3
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- Radiomics and Machine Learning in Medical Imaging 2
- Co-authors
- Zubair Shah (13 shared papers)Hazrat Ali (6 shared papers)Nady El Hajj (2 shared papers)Uzair Shah (4 shared papers)Noha A. Yousri (1 shared paper)Hamada R. H. Al-Absi (1 shared paper)Sulaiman Khan (2 shared papers)Md. Rafiul Biswas (4 shared papers)
In The Last Decade
Farida Mohsen
17 papers receiving 348 citations
Peers
Comparison fields: 5 of 76
- Health Informatics 56
- Health Information Management 41
- Radiology, Nuclear Medicine and Imaging 80
- Artificial Intelligence 108
- Neurology 22
Countries citing papers authored by Farida Mohsen
This map shows the geographic impact of Farida Mohsen'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 Farida Mohsen with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Farida Mohsen more than expected).
Fields of papers citing papers by Farida Mohsen
This network shows the impact of papers produced by Farida Mohsen. 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 Farida Mohsen. The network helps show where Farida Mohsen may publish in the future.
Co-authors
The 18 scholars most cited alongside Farida Mohsen, 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 | 2022 | 118 | |
| 2 | 2023 | 54 | |
| 3 | 2022 | 54 | |
| 4 | 2023 | 36 | |
| 5 | 2023 | 33 | |
| 6 | 2020 | 19 | |
| 7 | 2022 | 18 | |
| 8 | 2024 | 7 | |
| 9 | 2021 | 6 | |
| 10 | 2024 | 4 | |
| 11 | 2022 | 3 | |
| 12 | 2025 | 2 | |
| 13 | 2022 | 2 | |
| 14 | 2025 | 1 | |
| 15 | 2023 | 1 | |
| 16 | 2024 | 1 | |
| 17 | 2023 | 1 |
About Farida Mohsen
Farida Mohsen is a scholar working on Artificial Intelligence, Radiology, Nuclear Medicine and Imaging, Cardiology and Cardiovascular Medicine, Health Information Management and Pulmonary and Respiratory Medicine, having authored 17 papers that have together received 360 indexed citations. Recurring topics across this work include Machine Learning in Healthcare (3 papers), Artificial Intelligence in Healthcare (3 papers), Lung Cancer Diagnosis and Treatment (2 papers), Radiomics and Machine Learning in Medical Imaging (2 papers), Cardiovascular Function and Risk Factors (2 papers), Artificial Intelligence in Healthcare and Education (2 papers), EEG and Brain-Computer Interfaces (2 papers) and Functional Brain Connectivity Studies (2 papers). The work is most often cited by research in Health Informatics (56 citations), Health Information Management (41 citations), Radiology, Nuclear Medicine and Imaging (80 citations), Artificial Intelligence (108 citations) and Neurology (22 citations). Farida Mohsen has collaborated with scholars based in Qatar, China and Egypt. Frequent co-authors include Zubair Shah, Hazrat Ali, Nady El Hajj, Uzair Shah, Noha A. Yousri, Hamada R. H. Al-Absi, Sulaiman Khan, Md. Rafiul Biswas, Jiayang Wang and Kamal Al‐Sabahi. Their work appears in journals such as Scientific Reports, Applied Intelligence, Insights into Imaging, npj Digital Medicine and Journal of Personalized 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.