Guy Nadav
Impact in
- Health Informatics top 5%
- Artificial Intelligence in Healthcare and Education
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- MRI in cancer diagnosis
- Radiomics and Machine Learning in Medical Imaging
- Advanced MRI Techniques and Applications
Papers in
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- MRI in cancer diagnosis 6
- Advanced MRI Techniques and Applications 4
- Advanced Neuroimaging Techniques and Applications 3
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- Machine Learning in Bioinformatics 1
- Biomedical Text Mining and Ontologies 1
- Co-authors
- Nicole Fleischer (2 shared papers)Peter Krawitz (2 shared papers)Yaron Gurovich (1 shared paper)Karen W. Gripp (1 shared paper)Martin Zenker (1 shared paper)Omri Bar (1 shared paper)Lynne M. Bird (1 shared paper)Yair Hanani (1 shared paper)
- Journals
- Journal of Neuro-Oncology (2 papers)Magnetic Resonance Imaging (2 papers)Nature Medicine (1 paper)Neuroradiology (1 paper)NAR Genomics and Bioinformatics (1 paper)
- Partner nations
- IsraelGermanyUnited States
In The Last Decade
Guy Nadav
9 papers receiving 503 citations
Guy Nadav's Hit Papers
Peers
Comparison fields: 5 of 111
- Health Informatics 45
- Radiology, Nuclear Medicine and Imaging 132
- Genetics 151
- Genetics 50
- Health Information Management 14
Countries citing papers authored by Guy Nadav
This map shows the geographic impact of Guy Nadav'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 Guy Nadav with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Guy Nadav more than expected).
Fields of papers citing papers by Guy Nadav
This network shows the impact of papers produced by Guy Nadav. 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 Guy Nadav. The network helps show where Guy Nadav may publish in the future.
Co-authors
The 25 scholars most cited alongside Guy Nadav, 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 | Identifying facial phenotypes of genetic disorders using deep learning Hit paper breakdown → | 2018 | 411 |
| 2 | 2014 | 32 | |
| 3 | 2016 | 25 | |
| 4 | 2015 | 17 | |
| 5 | 2021 | 15 | |
| 6 | 2015 | 11 | |
| 7 | 2023 | 5 | |
| 8 | 2016 | 3 | |
| 9 | 2016 | 2 |
About Guy Nadav
Guy Nadav is a scholar working on Radiology, Nuclear Medicine and Imaging, Molecular Biology, Computer Vision and Pattern Recognition, Genetics and Artificial Intelligence, having authored 9 papers that have together received 521 indexed citations. Recurring topics across this work include MRI in cancer diagnosis (6 papers), Advanced MRI Techniques and Applications (4 papers), Advanced Neuroimaging Techniques and Applications (3 papers), Machine Learning in Bioinformatics (1 paper), Cleft Lip and Palate Research (1 paper), Brain Tumor Detection and Classification (1 paper), Biomedical Text Mining and Ontologies (1 paper) and Lanthanide and Transition Metal Complexes (1 paper). The work is most often cited by research in Health Informatics (45 citations), Radiology, Nuclear Medicine and Imaging (132 citations), Genetics (151 citations), Genetics (50 citations) and Health Information Management (14 citations). Guy Nadav has collaborated with scholars based in Israel, Germany and United States. Frequent co-authors include Nicole Fleischer, Peter Krawitz, Yaron Gurovich, Karen W. Gripp, Martin Zenker, Omri Bar, Lynne M. Bird, Yair Hanani, Lina Basel‐Salmon and Susanne B. Kamphausen. Their work appears in journals such as Journal of Neuro-Oncology, Magnetic Resonance Imaging, Nature Medicine, Neuroradiology and NAR Genomics and Bioinformatics.
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.