Monica Isgut
- Artificial Intelligence
- Health Informatics top 2%
- Molecular Biology
- Radiology, Nuclear Medicine and Imaging
- Public Health, Environmental and Occupational Health
- Topics
- Machine Learning in Healthcare (5 papers)COVID-19 diagnosis using AI (5 papers)Genetic Associations and Epidemiology (4 papers)
- Partner nations
- United StatesAustraliaBrazil
In The Last Decade
Monica Isgut
12 papers receiving 268 citations
Peers
Comparison fields: 5 of 83
- Artificial Intelligence 66
- Health Informatics 60
- Molecular Biology 55
- Radiology, Nuclear Medicine and Imaging 39
- Public Health, Environmental and Occupational Health 36
Countries citing papers authored by Monica Isgut
This map shows the geographic impact of Monica Isgut'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 Monica Isgut with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Monica Isgut more than expected).
Fields of papers citing papers by Monica Isgut
This network shows the impact of papers produced by Monica Isgut. 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 Monica Isgut. The network helps show where Monica Isgut may publish in the future.
Co-authorship network of co-authors of Monica Isgut
This figure shows the co-authorship network connecting the top 25 collaborators of Monica Isgut. A scholar is included among the top collaborators of Monica Isgut 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 Monica Isgut. Monica Isgut is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 1 | |
| 2 | 2 | |
| 3 | 0 | |
| 4 | 7 | |
| 5 | 4 | |
| 6 | 45 | |
| 7 | 0 | |
| 8 | 84 | |
| 9 | 17 | |
| 10 | 35 | |
| 11 | 5 | |
| 12 | 5 | |
| 13 | 35 | |
| 14 | 37 |
About Monica Isgut
Monica Isgut is a scholar working on Health Informatics, General Social Sciences and Artificial Intelligence, having authored 14 papers that have together received 277 indexed citations. Recurring topics across this work include Machine Learning in Healthcare (5 papers), COVID-19 diagnosis using AI (5 papers) and Genetic Associations and Epidemiology (4 papers). The work is most often cited by research in Health Informatics (60 citations), Health Information Management (15 citations) and Family Practice (5 citations). Monica Isgut has collaborated with scholars based in United States, Australia and Brazil. Frequent co-authors include May D. Wang, Felipe Giuste, Wenqi Shi, Tong Li, Yuanda Zhu, Ying Sha, Arshed A. Quyyumi, Jimeng Sun, Greg Gibson and Ömer Küçük. Their work appears in journals such as Scientific Reports, Medicinal Research Reviews and Genome Medicine.
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.