Anouk Stein
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
- Radiology practices and education
Papers in
-
- COVID-19 diagnosis using AI 3
- Radiomics and Machine Learning in Medical Imaging 3
- Radiology practices and education 2
- Surgery 4
- Pancreatitis Pathology and Treatment 2
- Co-authors
- George Shih (4 shared papers)Stephen Hobbs (2 shared papers)Archana Laroia (2 shared papers)Carol C. Wu (2 shared papers)Maya Galperin-Aizenberg (2 shared papers)Ritu R. Gill (2 shared papers)Jean Jeudy (2 shared papers)Kavitha Yaddanapudi (2 shared papers)
- Journals
- Journal of Digital Imaging (2 papers)Radiology Artificial Intelligence (1 paper)International Journal of Radiation Oncology*Biology*Physics (1 paper)Scientific Data (1 paper)Biology of Blood and Marrow Transplantation (1 paper)
- Partner nations
- United StatesCanadaAustralia
In The Last Decade
Anouk Stein
8 papers receiving 248 citations
Peers
Comparison fields: 5 of 37
- Health Informatics 46
- Radiology, Nuclear Medicine and Imaging 186
- Artificial Intelligence 114
- Transplantation 8
- Computer Vision and Pattern Recognition 49
Countries citing papers authored by Anouk Stein
This map shows the geographic impact of Anouk Stein'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 Anouk Stein with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Anouk Stein more than expected).
Fields of papers citing papers by Anouk Stein
This network shows the impact of papers produced by Anouk Stein. 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 Anouk Stein. The network helps show where Anouk Stein may publish in the future.
Co-authors
The 25 scholars most cited alongside Anouk Stein, 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 | 2019 | 181 | |
| 2 | 2019 | 34 | |
| 3 | 2021 | 10 | |
| 4 | 2004 | 10 | |
| 5 | 2001 | 10 | |
| 6 | 2022 | 7 | |
| 7 | 2006 | 3 | |
| 8 | 1998 | 2 | |
| 9 | 2024 | 0 |
About Anouk Stein
Anouk Stein is a scholar working on Radiology, Nuclear Medicine and Imaging, Surgery, Oncology, Pulmonary and Respiratory Medicine and Otorhinolaryngology, having authored 9 papers that have together received 257 indexed citations. Recurring topics across this work include COVID-19 diagnosis using AI (3 papers), Radiomics and Machine Learning in Medical Imaging (3 papers), Lung Cancer Diagnosis and Treatment (2 papers), Pancreatitis Pathology and Treatment (2 papers), Pancreatic and Hepatic Oncology Research (2 papers), Radiology practices and education (2 papers), Cancer Treatment and Pharmacology (1 paper) and Advanced Radiotherapy Techniques (1 paper). The work is most often cited by research in Health Informatics (46 citations), Radiology, Nuclear Medicine and Imaging (186 citations), Artificial Intelligence (114 citations), Transplantation (8 citations) and Computer Vision and Pattern Recognition (49 citations). Anouk Stein has collaborated with scholars based in United States, Canada and Australia. Frequent co-authors include George Shih, Stephen Hobbs, Archana Laroia, Carol C. Wu, Maya Galperin-Aizenberg, Ritu R. Gill, Jean Jeudy, Kavitha Yaddanapudi, Dharshan Vummidi and Palmi Shah. Their work appears in journals such as Journal of Digital Imaging, Radiology Artificial Intelligence, International Journal of Radiation Oncology*Biology*Physics, Scientific Data and Biology of Blood and Marrow Transplantation.
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.