Ural Koç
Impact in
- Health Informatics top 0.5%
- Artificial Intelligence in Healthcare and Education
-
- Radiomics and Machine Learning in Medical Imaging
- Radiology practices and education
- COVID-19 diagnosis using AI
Papers in
- Surgery 10
- Pancreatitis Pathology and Treatment 3
-
- Radiology practices and education 4
- Radiomics and Machine Learning in Medical Imaging 3
- Co-authors
- Tim Leiner (2 shared papers)Marc Zins (2 shared papers)Domenico Mastrodicasa (2 shared papers)Merel Huisman (2 shared papers)Jie Wu (2 shared papers)William Parker (2 shared papers)Sergey Morozov (2 shared papers)Martin Kočí (2 shared papers)
- Journals
- European Radiology (2 papers)Clinical Neurology and Neurosurgery (1 paper)Child s Nervous System (1 paper)Academic Radiology (1 paper)Biomarkers in Medicine (1 paper)
- Partner nations
- TürkiyeUnited StatesBelgium
In The Last Decade
Ural Koç
24 papers receiving 338 citations
Peers
Comparison fields: 5 of 69
- Health Informatics 189
- Radiology, Nuclear Medicine and Imaging 198
- Family Practice 9
- General Dentistry 5
- Artificial Intelligence 83
Countries citing papers authored by Ural Koç
This map shows the geographic impact of Ural Koç'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 Ural Koç with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Ural Koç more than expected).
Fields of papers citing papers by Ural Koç
This network shows the impact of papers produced by Ural Koç. 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 Ural Koç. The network helps show where Ural Koç may publish in the future.
Co-authors
The 25 scholars most cited alongside Ural Koç, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
Showing the 20 most-cited of 29 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | 2021 | 143 | |
| 2 | 2021 | 91 | |
| 3 | 2020 | 16 | |
| 4 | 2022 | 14 | |
| 5 | 2017 | 13 | |
| 6 | 2020 | 8 | |
| 7 | 2018 | 8 | |
| 8 | 2021 | 8 | |
| 9 | 2020 | 7 | |
| 10 | 2021 | 7 | |
| 11 | 2019 | 5 | |
| 12 | 2020 | 4 | |
| 13 | 2022 | 3 | |
| 14 | 2016 | 2 | |
| 15 | 2020 | 2 | |
| 16 | 2025 | 2 | |
| 17 | 2018 | 2 | |
| 18 | 2020 | 1 | |
| 19 | 2017 | 1 | |
| 20 | 2025 | 1 |
About Ural Koç
Ural Koç is a scholar working on Surgery, Radiology, Nuclear Medicine and Imaging, Pulmonary and Respiratory Medicine, Health Informatics and Epidemiology, having authored 29 papers that have together received 343 indexed citations. Recurring topics across this work include Artificial Intelligence in Healthcare and Education (6 papers), Liver Disease Diagnosis and Treatment (6 papers), Radiology practices and education (4 papers), Pancreatitis Pathology and Treatment (3 papers), Vascular Procedures and Complications (3 papers), Radiomics and Machine Learning in Medical Imaging (3 papers), Machine Learning in Healthcare (2 papers) and Pancreatic and Hepatic Oncology Research (2 papers). The work is most often cited by research in Health Informatics (189 citations), Radiology, Nuclear Medicine and Imaging (198 citations), Family Practice (9 citations), General Dentistry (5 citations) and Artificial Intelligence (83 citations). Ural Koç has collaborated with scholars based in Türkiye, United States and Belgium. Frequent co-authors include Tim Leiner, Marc Zins, Domenico Mastrodicasa, Merel Huisman, Jie Wu, William Parker, Sergey Morozov, Martin Kočí, Martin J. Willemink and Erik Ranschaert. Their work appears in journals such as European Radiology, Clinical Neurology and Neurosurgery, Child s Nervous System, Academic Radiology and Biomarkers in 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.