Maha Shady
Impact in
- Health Informatics top 2%
- Artificial Intelligence in Healthcare and Education
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- Radiomics and Machine Learning in Medical Imaging
Papers in
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- AI in cancer detection 4
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- Radiomics and Machine Learning in Medical Imaging 4
- Co-authors
- Tiffany Chen (7 shared papers)Faisal Mahmood (6 shared papers)Ming Y. Lu (7 shared papers)Richard J. Chen (5 shared papers)Drew F. K. Williamson (4 shared papers)Jana Lipková (6 shared papers)Mane Williams (4 shared papers)Muhammad Shaban (2 shared papers)
- Journals
- Journal of Clinical Oncology (2 papers)Journal of Pathology Informatics (2 papers)Nature Medicine (1 paper)Clinical Cancer Research (1 paper)QJM (1 paper)
- Partner nations
- United StatesSwitzerlandEgypt
In The Last Decade
Maha Shady
10 papers receiving 518 citations
Maha Shady's Hit Papers
Peers
Comparison fields: 5 of 63
- Health Informatics 57
- Radiology, Nuclear Medicine and Imaging 270
- Artificial Intelligence 354
- Biophysics 48
- Cancer Research 98
Countries citing papers authored by Maha Shady
This map shows the geographic impact of Maha Shady'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 Maha Shady with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Maha Shady more than expected).
Fields of papers citing papers by Maha Shady
This network shows the impact of papers produced by Maha Shady. 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 Maha Shady. The network helps show where Maha Shady may publish in the future.
Co-authors
The 25 scholars most cited alongside Maha Shady, 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 | Pan-cancer integrative histology-genomic analysis via multimodal deep learning Hit paper breakdown → | 2022 | 291 |
| 2 | 2021 | 152 | |
| 3 | 2022 | 52 | |
| 4 | 2022 | 11 | |
| 5 | 2022 | 5 | |
| 6 | 2021 | 4 | |
| 7 | 2017 | 3 | |
| 8 | 2024 | 2 | |
| 9 | 2021 | 1 | |
| 10 | 2017 | 1 |
About Maha Shady
Maha Shady is a scholar working on Artificial Intelligence, Radiology, Nuclear Medicine and Imaging, Surgery, Cancer Research and Molecular Biology, having authored 10 papers that have together received 522 indexed citations. Recurring topics across this work include AI in cancer detection (4 papers), Radiomics and Machine Learning in Medical Imaging (4 papers), Cancer Genomics and Diagnostics (3 papers), Molecular Biology Techniques and Applications (2 papers), Transplantation: Methods and Outcomes (2 papers), Thyroid Disorders and Treatments (1 paper), Mechanical Circulatory Support Devices (1 paper) and Genetic factors in colorectal cancer (1 paper). The work is most often cited by research in Health Informatics (57 citations), Radiology, Nuclear Medicine and Imaging (270 citations), Artificial Intelligence (354 citations), Biophysics (48 citations) and Cancer Research (98 citations). Maha Shady has collaborated with scholars based in United States, Switzerland and Egypt. Frequent co-authors include Tiffany Chen, Faisal Mahmood, Ming Y. Lu, Richard J. Chen, Drew F. K. Williamson, Jana Lipková, Mane Williams, Muhammad Shaban, Trevor Manz and Wei‐Hung Weng. Their work appears in journals such as Journal of Clinical Oncology, Journal of Pathology Informatics, Nature Medicine, Clinical Cancer Research and QJM.
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.