David Spak
Impact in
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- Radiomics and Machine Learning in Medical Imaging
- MRI in cancer diagnosis
- Medical Imaging Techniques and Applications
Papers in
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- AI in cancer detection 3
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- MRI in cancer diagnosis 2
- Radiomics and Machine Learning in Medical Imaging 1
- Medical Imaging Techniques and Applications 1
- Advanced MRI Techniques and Applications 1
- Co-authors
- Lumarie Santiago (2 shared papers)Mark J. Dryden (2 shared papers)Başak E. Doğan (2 shared papers)Beatriz E. Adrada (4 shared papers)Gaiane M. Rauch (3 shared papers)Gary J. Whitman (2 shared papers)Alyssa G. Rieber (1 shared paper)Lewis E. Foxhall (1 shared paper)
- Journals
- Academic Radiology (2 papers)Radiographics (1 paper)Radiology Artificial Intelligence (1 paper)Ultrasound in Medicine & Biology (1 paper)Diagnostic and Interventional Imaging (1 paper)
- Partner nations
- United States
In The Last Decade
David Spak
8 papers receiving 342 citations
David Spak's Hit Papers
Peers
Comparison fields: 5 of 46
- Radiology, Nuclear Medicine and Imaging 134
- Health Informatics 5
- Artificial Intelligence 107
- Cancer Research 40
- Pathology and Forensic Medicine 47
Countries citing papers authored by David Spak
This map shows the geographic impact of David Spak'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 David Spak with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites David Spak more than expected).
Fields of papers citing papers by David Spak
This network shows the impact of papers produced by David Spak. 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 David Spak. The network helps show where David Spak may publish in the future.
Co-authors
The 25 scholars most cited alongside David Spak, 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 | BI-RADS ® fifth edition: A summary of changes Hit paper breakdown → | 2017 | 298 |
| 2 | 2021 | 18 | |
| 3 | 2020 | 12 | |
| 4 | 2022 | 9 | |
| 5 | 2023 | 8 | |
| 6 | 2022 | 3 | |
| 7 | 2019 | 1 | |
| 8 | 2019 | 1 | |
| 9 | 日本語版ダイジェスト USGA Green Section RECORD スープリのための新しい道具 | 2005 | 0 |
| 10 | Security Best Practices Efforts and Documents | 2013 | 0 |
About David Spak
David Spak is a scholar working on Artificial Intelligence, Radiology, Nuclear Medicine and Imaging, Pathology and Forensic Medicine, Oncology and Cancer Research, having authored 10 papers that have together received 350 indexed citations. Recurring topics across this work include AI in cancer detection (3 papers), Breast Cancer Treatment Studies (2 papers), MRI in cancer diagnosis (2 papers), Global Cancer Incidence and Screening (2 papers), Breast Lesions and Carcinomas (2 papers), Radiomics and Machine Learning in Medical Imaging (1 paper), Medical Imaging Techniques and Applications (1 paper) and Advanced MRI Techniques and Applications (1 paper). The work is most often cited by research in Radiology, Nuclear Medicine and Imaging (134 citations), Health Informatics (5 citations), Artificial Intelligence (107 citations), Cancer Research (40 citations) and Pathology and Forensic Medicine (47 citations). David Spak has collaborated with scholars based in United States. Frequent co-authors include Lumarie Santiago, Mark J. Dryden, Başak E. Doğan, Beatriz E. Adrada, Gaiane M. Rauch, Gary J. Whitman, Alyssa G. Rieber, Lewis E. Foxhall, Mark A. Helvie and Mary S. Guirguis. Their work appears in journals such as Academic Radiology, Radiographics, Radiology Artificial Intelligence, Ultrasound in Medicine & Biology and Diagnostic and Interventional Imaging.
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.