Pragnya Maduskar
- Radiology, Nuclear Medicine and Imaging top 5%
- Infectious Diseases top 10%
- Artificial Intelligence top 10%
- Computer Vision and Pattern Recognition top 10%
- Pulmonary and Respiratory Medicine
- Co-authors
- Bram van GinnekenClara I. SánchezRick H. H. M. PhilipsenJaime MelendezLaurens HogewegHelen AylesRodney DawsonGrant Theron
- Topics
- COVID-19 diagnosis using AI (15 papers)Tuberculosis Research and Epidemiology (7 papers)Image Processing Techniques and Applications (5 papers)
- Partner nations
- NetherlandsUnited KingdomZambia
In The Last Decade
Pragnya Maduskar
19 papers receiving 591 citations
Peers
Comparison fields: 5 of 66
- Radiology, Nuclear Medicine and Imaging 465
- Infectious Diseases 164
- Artificial Intelligence 162
- Computer Vision and Pattern Recognition 131
- Pulmonary and Respiratory Medicine 120
Countries citing papers authored by Pragnya Maduskar
This map shows the geographic impact of Pragnya Maduskar'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 Pragnya Maduskar with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Pragnya Maduskar more than expected).
Fields of papers citing papers by Pragnya Maduskar
This network shows the impact of papers produced by Pragnya Maduskar. 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 Pragnya Maduskar. The network helps show where Pragnya Maduskar may publish in the future.
Co-authorship network of co-authors of Pragnya Maduskar
This figure shows the co-authorship network connecting the top 25 collaborators of Pragnya Maduskar. A scholar is included among the top collaborators of Pragnya Maduskar 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 Pragnya Maduskar. Pragnya Maduskar is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 5 | |
| 2 | 99 | |
| 3 | 53 | |
| 4 | 26 | |
| 5 | 40 | |
| 6 | 57 | |
| 7 | 33 | |
| 8 | 6 | |
| 9 | 67 | |
| 10 | 10 | |
| 11 | 94 | |
| 12 | 3 | |
| 13 | 57 | |
| 14 | 14 | |
| 15 | 7 | |
| 16 | 5 | |
| 17 | 31 | |
| 18 | 1 | |
| 19 | 10 |
About Pragnya Maduskar
Pragnya Maduskar is a scholar working on Radiology, Nuclear Medicine and Imaging, Media Technology and Infectious Diseases, having authored 19 papers that have together received 618 indexed citations. Recurring topics across this work include COVID-19 diagnosis using AI (15 papers), Tuberculosis Research and Epidemiology (7 papers) and Image Processing Techniques and Applications (5 papers). The work is most often cited by research in Health Informatics (46 citations), Radiology, Nuclear Medicine and Imaging (465 citations) and Infectious Diseases (164 citations). Pragnya Maduskar has collaborated with scholars based in Netherlands, United Kingdom and Zambia. Frequent co-authors include Bram van Ginneken, Clara I. Sánchez, Rick H. H. M. Philipsen, Jaime Melendez, Laurens Hogeweg, Helen Ayles, Rodney Dawson, Grant Theron, Keertan Dheda and Liesbeth Peters-Bax. Their work appears in journals such as PLoS ONE, Scientific Reports and IEEE Transactions on Medical Imaging.
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.