Roberto Lo Gullo
- Radiology, Nuclear Medicine and Imaging top 2%
- Pulmonary and Respiratory Medicine top 10%
- Artificial Intelligence top 5%
- Surgery
- Biomedical Engineering
- Co-authors
- Katja PinkerIsaac Daimiel NaranjoElizabeth A. MorrisMannudeep K. KalraSubba R. DigumarthySunitha B. ThakurCarolina Rossi SaccarelliAlmir Galvão Vieira Bitencourt
- Topics
- Radiomics and Machine Learning in Medical Imaging (24 papers)MRI in cancer diagnosis (21 papers)AI in cancer detection (9 papers)
- Partner nations
- United StatesItalyAustria
In The Last Decade
Roberto Lo Gullo
55 papers receiving 1.1k citations
Peers
Comparison fields: 5 of 84
- Radiology, Nuclear Medicine and Imaging 780
- Pulmonary and Respiratory Medicine 254
- Artificial Intelligence 253
- Surgery 225
- Biomedical Engineering 170
Countries citing papers authored by Roberto Lo Gullo
This map shows the geographic impact of Roberto Lo Gullo'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 Roberto Lo Gullo with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Roberto Lo Gullo more than expected).
Fields of papers citing papers by Roberto Lo Gullo
This network shows the impact of papers produced by Roberto Lo Gullo. 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 Roberto Lo Gullo. The network helps show where Roberto Lo Gullo may publish in the future.
Co-authorship network of co-authors of Roberto Lo Gullo
This figure shows the co-authorship network connecting the top 25 collaborators of Roberto Lo Gullo. A scholar is included among the top collaborators of Roberto Lo Gullo 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 Roberto Lo Gullo. Roberto Lo Gullo is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 0 | |
| 2 | 0 | |
| 3 | 6 | |
| 4 | 1 | |
| 5 | 47 | |
| 6 | 7 | |
| 7 | 2 | |
| 8 | 23 | |
| 9 | 2 | |
| 10 | 9 | |
| 11 | 41 | |
| 12 | 49 | |
| 13 | 2 | |
| 14 | 9 | |
| 15 | 28 | |
| 16 | 123 | |
| 17 | 5 | |
| 18 | 14 | |
| 19 | 6 | |
| 20 | 27 |
About Roberto Lo Gullo
Roberto Lo Gullo is a scholar working on Radiology, Nuclear Medicine and Imaging, Gastroenterology and Health Informatics, having authored 58 papers that have together received 1.2k indexed citations. Recurring topics across this work include Radiomics and Machine Learning in Medical Imaging (24 papers), MRI in cancer diagnosis (21 papers) and AI in cancer detection (9 papers). The work is most often cited by research in Health Informatics (75 citations), Radiology, Nuclear Medicine and Imaging (780 citations) and Gastroenterology (100 citations). Roberto Lo Gullo has collaborated with scholars based in United States, Italy and Austria. Frequent co-authors include Katja Pinker, Isaac Daimiel Naranjo, Elizabeth A. Morris, Mannudeep K. Kalra, Subba R. Digumarthy, Sunitha B. Thakur, Carolina Rossi Saccarelli, Almir Galvão Vieira Bitencourt, Maxine S. Jochelson and Alexi Otrakji. Their work appears in journals such as Gastroenterology, Radiology and American Journal of Roentgenology.
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.