Ștefan Udriștoiu
- Radiology, Nuclear Medicine and Imaging
- Artificial Intelligence
- Oncology
- Molecular Medicine top 10%
- Epidemiology
- Co-authors
- Gabriel GruionuLucian Gheorghe GruionuAdrian SăftoiuAlice Elena GheneaCorina Maria VasileMihaela PopescuOvidiu ZlatianAlina Constantin
- Topics
- AI in cancer detection (7 papers)Image Retrieval and Classification Techniques (6 papers)Liver Disease Diagnosis and Treatment (5 papers)
- Partner nations
- RomaniaUnited StatesVietnam
In The Last Decade
Ștefan Udriștoiu
29 papers receiving 243 citations
Peers
Comparison fields: 5 of 70
- Radiology, Nuclear Medicine and Imaging 68
- Artificial Intelligence 57
- Oncology 56
- Molecular Medicine 50
- Epidemiology 48
Countries citing papers authored by Ștefan Udriștoiu
This map shows the geographic impact of Ștefan Udriștoiu'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 Ștefan Udriștoiu with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Ștefan Udriștoiu more than expected).
Fields of papers citing papers by Ștefan Udriștoiu
This network shows the impact of papers produced by Ștefan Udriștoiu. 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 Ștefan Udriștoiu. The network helps show where Ștefan Udriștoiu may publish in the future.
Co-authorship network of co-authors of Ștefan Udriștoiu
This figure shows the co-authorship network connecting the top 25 collaborators of Ștefan Udriștoiu. A scholar is included among the top collaborators of Ștefan Udriștoiu based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Ștefan Udriștoiu. Ștefan Udriștoiu is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 1 | |
| 2 | 1 | |
| 3 | 2 | |
| 4 | 2 | |
| 5 | 6 | |
| 6 | 39 | |
| 7 | 9 | |
| 8 | 1 | |
| 9 | 1 | |
| 10 | 3 | |
| 11 | 25 | |
| 12 | 17 | |
| 13 | 26 | |
| 14 | 20 | |
| 15 | 3 | |
| 16 | 44 | |
| 17 | 30 | |
| 18 | An experimental framework for learning the medical image diagnosis | 2 |
| 19 | 1 | |
| 20 | Establishing Medical Diagnosis using Pattern Semantic Rules | 1 |
About Ștefan Udriștoiu
Ștefan Udriștoiu is a scholar working on Health Informatics, Hepatology and Applied Microbiology and Biotechnology, having authored 32 papers that have together received 254 indexed citations. Recurring topics across this work include AI in cancer detection (7 papers), Image Retrieval and Classification Techniques (6 papers) and Liver Disease Diagnosis and Treatment (5 papers). The work is most often cited by research in Health Informatics (17 citations), Molecular Medicine (50 citations) and Applied Microbiology and Biotechnology (19 citations). Ștefan Udriștoiu has collaborated with scholars based in Romania, United States and Vietnam. Frequent co-authors include Gabriel Gruionu, Lucian Gheorghe Gruionu, Adrian Săftoiu, Alice Elena Ghenea, Corina Maria Vasile, Mihaela Popescu, Ovidiu Zlatian, Alina Constantin, Ramona Cioboată and Bogdan Silviu Ungureanu. Their work appears in journals such as Gastroenterology, PLoS ONE and Gastrointestinal Endoscopy.
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.