Maria Elena Laino
- Health Informatics top 5%
-
- Radiomics and Machine Learning in Medical Imaging 11
- COVID-19 diagnosis using AI 4
- Advanced MRI Techniques and Applications 3
-
- Pancreatic and Hepatic Oncology Research 4
-
- AI in cancer detection 3
- Machine Learning in Healthcare 2
-
- COVID-19 Clinical Research Studies 3
-
- Advanced X-ray and CT Imaging 3
- Co-authors
- Victor SavevskiLuca SabaTommaso TartaglioneAntonella GiampietroSabrina ChiloiroLaura De MarinisAntonio BianchiAndrei I. Holodny
- Cited by
- Health InformaticsRadiology, Nuclear Medicine and ImagingEndocrinology, Diabetes and Metabolism
- Partner nations
- ItalyUnited StatesUnited Kingdom
In The Last Decade
Maria Elena Laino
26 papers receiving 337 citations
Peers
Comparison fields: 5 of 70
- Health Informatics 24
- Radiology, Nuclear Medicine and Imaging 146
- Endocrinology, Diabetes and Metabolism 55
- Genetics 29
- Cognitive Neuroscience 40
Countries citing papers authored by Maria Elena Laino
This map shows the geographic impact of Maria Elena Laino'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 Maria Elena Laino with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Maria Elena Laino more than expected).
Fields of papers citing papers by Maria Elena Laino
This network shows the impact of papers produced by Maria Elena Laino. 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 Maria Elena Laino. The network helps show where Maria Elena Laino may publish in the future.
Co-authorship network
The 25 scholars most cited alongside Maria Elena Laino, 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 | 2024 | 1 | |
| 2 | 2023 | 12 | |
| 3 | 2023 | 8 | |
| 4 | 2023 | 21 | |
| 5 | 2022 | 4 | |
| 6 | 2022 | 9 | |
| 7 | 2022 | 14 | |
| 8 | 2022 | 12 | |
| 9 | 2022 | 0 | |
| 10 | 2022 | 20 | |
| 11 | 2022 | 7 | |
| 12 | 2021 | 7 | |
| 13 | 2021 | 17 | |
| 14 | 2020 | 16 | |
| 15 | 2019 | 29 | |
| 16 | 2018 | 22 | |
| 17 | 2018 | 5 | |
| 18 | 2016 | 12 | |
| 19 | 2016 | 37 | |
| 20 | 2011 | 20 |
About Maria Elena Laino
Maria Elena Laino is a scholar working on Radiology, Nuclear Medicine and Imaging, Health Informatics and Oncology, having authored 28 papers that have together received 344 indexed citations. Recurring topics across this work include Radiomics and Machine Learning in Medical Imaging (11 papers), Pancreatic and Hepatic Oncology Research (4 papers), COVID-19 diagnosis using AI (4 papers), AI in cancer detection (3 papers), COVID-19 Clinical Research Studies (3 papers), Advanced MRI Techniques and Applications (3 papers), Advanced X-ray and CT Imaging (3 papers) and Machine Learning in Healthcare (2 papers). The work is most often cited by research in Health Informatics (24 citations), Radiology, Nuclear Medicine and Imaging (146 citations) and Endocrinology, Diabetes and Metabolism (55 citations). Maria Elena Laino has collaborated with scholars based in Italy, United States and United Kingdom. Frequent co-authors include Victor Savevski, Luca Saba, Tommaso Tartaglione, Antonella Giampietro, Sabrina Chiloiro, Laura De Marinis, Antonio Bianchi, Andrei I. Holodny, Behroze Vachha and Kyung K. Peck.
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.