Silvia Burti
Impact in
- Health Informatics top 5%
- Artificial Intelligence in Healthcare and Education
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- Radiomics and Machine Learning in Medical Imaging
- COVID-19 diagnosis using AI
Papers in
-
- Radiomics and Machine Learning in Medical Imaging 6
- MRI in cancer diagnosis 3
- Radiology practices and education 3
- COVID-19 diagnosis using AI 3
- Co-authors
- Tommaso Banzato (19 shared papers)Alessandro Zotti (17 shared papers)Federico Bonsembiante (9 shared papers)Marek Wodziński (5 shared papers)Barbara Contiero (6 shared papers)Manfredo Atzori (2 shared papers)Maria Elena Gelain (1 shared paper)Luca Aresu (1 shared paper)
- Journals
- Scientific Reports (6 papers)Frontiers in Veterinary Science (5 papers)The Veterinary Journal (3 papers)Veterinary Record (3 papers)Research in Veterinary Science (2 papers)
- Partner nations
- ItalySwitzerlandPoland
In The Last Decade
Silvia Burti
20 papers receiving 274 citations
Peers
Comparison fields: 5 of 69
- Health Informatics 44
- Radiology, Nuclear Medicine and Imaging 112
- Small Animals 28
- Geriatrics and Gerontology 12
- Equine 4
Countries citing papers authored by Silvia Burti
This map shows the geographic impact of Silvia Burti'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 Silvia Burti with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Silvia Burti more than expected).
Fields of papers citing papers by Silvia Burti
This network shows the impact of papers produced by Silvia Burti. 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 Silvia Burti. The network helps show where Silvia Burti may publish in the future.
Co-authors
The 25 scholars most cited alongside Silvia Burti, 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 | 2020 | 48 | |
| 2 | 2021 | 45 | |
| 3 | 2018 | 36 | |
| 4 | 2019 | 35 | |
| 5 | 2012 | 19 | |
| 6 | 2024 | 19 | |
| 7 | 2022 | 13 | |
| 8 | 2021 | 11 | |
| 9 | 2020 | 11 | |
| 10 | 2019 | 9 | |
| 11 | 2023 | 8 | |
| 12 | 2023 | 6 | |
| 13 | 2023 | 5 | |
| 14 | 2022 | 5 | |
| 15 | 2021 | 5 | |
| 16 | 2020 | 3 | |
| 17 | 2022 | 2 | |
| 18 | 2025 | 1 | |
| 19 | 2024 | 1 | |
| 20 | 2025 | 1 |
About Silvia Burti
Silvia Burti is a scholar working on Radiology, Nuclear Medicine and Imaging, Pulmonary and Respiratory Medicine, Hepatology, Small Animals and Epidemiology, having authored 20 papers that have together received 283 indexed citations. Recurring topics across this work include Radiomics and Machine Learning in Medical Imaging (6 papers), Hepatocellular Carcinoma Treatment and Prognosis (5 papers), MRI in cancer diagnosis (3 papers), Liver Disease Diagnosis and Treatment (3 papers), Radiology practices and education (3 papers), COVID-19 diagnosis using AI (3 papers), AI in cancer detection (3 papers) and Veterinary Medicine and Surgery (2 papers). The work is most often cited by research in Health Informatics (44 citations), Radiology, Nuclear Medicine and Imaging (112 citations), Small Animals (28 citations), Geriatrics and Gerontology (12 citations) and Equine (4 citations). Silvia Burti has collaborated with scholars based in Italy, Switzerland and Poland. Frequent co-authors include Tommaso Banzato, Alessandro Zotti, Federico Bonsembiante, Marek Wodziński, Barbara Contiero, Manfredo Atzori, Maria Elena Gelain, Luca Aresu, Marco Canevelli and Matteo Cesari. Their work appears in journals such as Scientific Reports, Frontiers in Veterinary Science, The Veterinary Journal, Veterinary Record and Research in Veterinary Science.
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.