Thushika Mahendiran
- Radiology, Nuclear Medicine and Imaging top 5%
- Health Informatics top 0.1%
- Artificial Intelligence top 5%
- Biomedical Engineering
- Health Information Management top 2%
- Co-authors
- Aditya U. KaleKonstantinos BalaskasPearse A. KeaneMohith ShamdasLucas M. BachmannAlastair K. DennistonXiaoxuan LiuDun Jack Fu
- Topics
- COVID-19 diagnosis using AI (1 paper)Artificial Intelligence in Healthcare and Education (1 paper)Cutaneous Melanoma Detection and Management (1 paper)
- Partner nations
- United KingdomSwitzerlandGermany
In The Last Decade
Thushika Mahendiran
1 paper receiving 998 citations
Hit Papers
Peers
Comparison fields: 5 of 122
- Radiology, Nuclear Medicine and Imaging 470
- Health Informatics 447
- Artificial Intelligence 367
- Biomedical Engineering 129
- Health Information Management 99
Countries citing papers authored by Thushika Mahendiran
This map shows the geographic impact of Thushika Mahendiran'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 Thushika Mahendiran with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Thushika Mahendiran more than expected).
Fields of papers citing papers by Thushika Mahendiran
This network shows the impact of papers produced by Thushika Mahendiran. 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 Thushika Mahendiran. The network helps show where Thushika Mahendiran may publish in the future.
Co-authorship network of co-authors of Thushika Mahendiran
This figure shows the co-authorship network connecting the top 25 collaborators of Thushika Mahendiran. A scholar is included among the top collaborators of Thushika Mahendiran 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 Thushika Mahendiran. Thushika Mahendiran 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 | A comparison of deep learning performance against health-care professionals in detecting diseases from medical imaging: a systematic review and meta-analysisbreakdown → | 1020 |
About Thushika Mahendiran
Thushika Mahendiran is a scholar working on Health Informatics, Radiology, Nuclear Medicine and Imaging and Oncology, having authored 2 papers that have together received 1.0k indexed citations. Recurring topics across this work include COVID-19 diagnosis using AI (1 paper), Artificial Intelligence in Healthcare and Education (1 paper) and Cutaneous Melanoma Detection and Management (1 paper). The work is most often cited by research in Health Informatics (447 citations), Health Information Management (99 citations) and Radiology, Nuclear Medicine and Imaging (470 citations). Thushika Mahendiran has collaborated with scholars based in United Kingdom, Switzerland and Germany. Frequent co-authors include Aditya U. Kale, Konstantinos Balaskas, Pearse A. Keane, Mohith Shamdas, Lucas M. Bachmann, Alastair K. Denniston, Xiaoxuan Liu, Dun Jack Fu, Livia Faes and Siegfried K. Wagner. Their work appears in journals such as British Journal of Dermatology and The Lancet Digital Health.
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.