Massimo Salvi
Impact in
- Health Informatics top 2%
- Artificial Intelligence in Healthcare and Education
- Biophysics top 5%
- Cell Image Analysis Techniques
Papers in
-
- AI in cancer detection 24
- Biophysics 10
- Cell Image Analysis Techniques 9
- Co-authors
- Filippo Molinari (69 shared papers)U. Rajendra Acharya (33 shared papers)Kristen M. Meiburger (32 shared papers)Silvia Seoni (21 shared papers)Prabal Datta Barua (19 shared papers)Nicola Michielli (9 shared papers)Luca Molinaro (7 shared papers)Mauro Giulio Papotti (5 shared papers)
In The Last Decade
Massimo Salvi
83 papers receiving 1.6k citations
Massimo Salvi's Hit Papers
Peers
Comparison fields: 5 of 143
- Health Informatics 65
- Biophysics 117
- Radiology, Nuclear Medicine and Imaging 407
- Artificial Intelligence 561
- Computer Vision and Pattern Recognition 322
Countries citing papers authored by Massimo Salvi
This map shows the geographic impact of Massimo Salvi'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 Massimo Salvi with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Massimo Salvi more than expected).
Fields of papers citing papers by Massimo Salvi
This network shows the impact of papers produced by Massimo Salvi. 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 Massimo Salvi. The network helps show where Massimo Salvi may publish in the future.
Co-authors
The 25 scholars most cited alongside Massimo Salvi, 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 96 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | 2020 | 267 | |
| 2 | Application of uncertainty quantification to artificial intelligence in healthcare: A review of last decade (2013–2023) Hit paper breakdown → | 2023 | 128 |
| 3 | 2023 | 96 | |
| 4 | 2020 | 93 | |
| 5 | 2022 | 73 | |
| 6 | 2021 | 56 | |
| 7 | 2018 | 48 | |
| 8 | 2016 | 45 | |
| 9 | 2021 | 44 | |
| 10 | 2024 | 39 | |
| 11 | 2024 | 35 | |
| 12 | 2019 | 34 | |
| 13 | 2024 | 32 | |
| 14 | 2024 | 31 | |
| 15 | 2022 | 30 | |
| 16 | 2024 | 28 | |
| 17 | 2022 | 28 | |
| 18 | 2025 | 27 | |
| 19 | 2020 | 27 | |
| 20 | 2021 | 26 |
About Massimo Salvi
Massimo Salvi is a scholar working on Artificial Intelligence, Biophysics, Computer Vision and Pattern Recognition, Radiology, Nuclear Medicine and Imaging and Biomedical Engineering, having authored 96 papers that have together received 1.7k indexed citations. Recurring topics across this work include AI in cancer detection (24 papers), Cell Image Analysis Techniques (9 papers), Radiomics and Machine Learning in Medical Imaging (7 papers), Cutaneous Melanoma Detection and Management (6 papers), Digital Imaging for Blood Diseases (5 papers), ECG Monitoring and Analysis (5 papers), Medical Image Segmentation Techniques (5 papers) and Photoacoustic and Ultrasonic Imaging (5 papers). The work is most often cited by research in Health Informatics (65 citations), Biophysics (117 citations), Radiology, Nuclear Medicine and Imaging (407 citations), Artificial Intelligence (561 citations) and Computer Vision and Pattern Recognition (322 citations). Massimo Salvi has collaborated with scholars based in Italy, Australia and Singapore. Frequent co-authors include Filippo Molinari, U. Rajendra Acharya, Kristen M. Meiburger, Silvia Seoni, Prabal Datta Barua, Nicola Michielli, Luca Molinaro, Mauro Giulio Papotti, Alessandro Gambella and Jahmunah Vicnesh. Their work appears in journals such as IEEE Access, Computers in Biology and Medicine, International Journal of Imaging Systems and Technology, Computer Methods and Programs in Biomedicine and Information Fusion.
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.