Michail Mamalakis
- Radiology, Nuclear Medicine and Imaging top 10%
- Artificial Intelligence
- Infectious Diseases
- Biomedical Engineering
- Health Informatics top 5%
- Co-authors
- Surajit RayAbhirup BanerjeeLouise S. MackenzieBart VorselaarsMark BakerAndrew J. SwiftSamer AlabedPankaj Garg
- Topics
- Cardiac Imaging and Diagnostics (5 papers)COVID-19 diagnosis using AI (4 papers)Radiomics and Machine Learning in Medical Imaging (3 papers)
- Journals
- SHILAP Revista de lepidopterologíaScientific ReportsRadiology
- Partner nations
- United KingdomNetherlandsUnited States
In The Last Decade
Michail Mamalakis
17 papers receiving 239 citations
Peers
Comparison fields: 5 of 57
- Radiology, Nuclear Medicine and Imaging 157
- Artificial Intelligence 70
- Infectious Diseases 55
- Biomedical Engineering 37
- Health Informatics 37
Countries citing papers authored by Michail Mamalakis
This map shows the geographic impact of Michail Mamalakis'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 Michail Mamalakis with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Michail Mamalakis more than expected).
Fields of papers citing papers by Michail Mamalakis
This network shows the impact of papers produced by Michail Mamalakis. 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 Michail Mamalakis. The network helps show where Michail Mamalakis may publish in the future.
Co-authorship network of co-authors of Michail Mamalakis
This figure shows the co-authorship network connecting the top 25 collaborators of Michail Mamalakis. A scholar is included among the top collaborators of Michail Mamalakis 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 Michail Mamalakis. Michail Mamalakis 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 | 1 | |
| 3 | 1 | |
| 4 | 12 | |
| 5 | 6 | |
| 6 | 0 | |
| 7 | 9 | |
| 8 | 15 | |
| 9 | 12 | |
| 10 | 10 | |
| 11 | 4 | |
| 12 | 17 | |
| 13 | 4 | |
| 14 | 1 | |
| 15 | 4 | |
| 16 | 17 | |
| 17 | 8 | |
| 18 | 120 | |
| 19 | 4 |
About Michail Mamalakis
Michail Mamalakis is a scholar working on Health Informatics, Radiology, Nuclear Medicine and Imaging and Internal Medicine, having authored 19 papers that have together received 245 indexed citations. Recurring topics across this work include Cardiac Imaging and Diagnostics (5 papers), COVID-19 diagnosis using AI (4 papers) and Radiomics and Machine Learning in Medical Imaging (3 papers). The work is most often cited by research in Health Informatics (37 citations), Radiology, Nuclear Medicine and Imaging (157 citations) and Health Information Management (21 citations). Michail Mamalakis has collaborated with scholars based in United Kingdom, Netherlands and United States. Frequent co-authors include Surajit Ray, Abhirup Banerjee, Louise S. Mackenzie, Bart Vorselaars, Mark Baker, Andrew J. Swift, Samer Alabed, Pankaj Garg, Krit Dwivedi and Michael Sharkey. Their work appears in journals such as SHILAP Revista de lepidopterología, Scientific Reports and Radiology.
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.