Kabilan Elangovan
- Health Informatics top 0.05%
- Artificial Intelligence in Healthcare and Education 12
- Family Practice top 5%
- Artificial Intelligence top 2%
- Machine Learning in Healthcare 5
- AI in cancer detection 3
- Topic Modeling 2
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- Radiomics and Machine Learning in Medical Imaging 4
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- Sustainable Supply Chain Management 3
- Quality and Management Systems 3
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- Quality and Supply Management 2
- Co-authors
- Daniel Shu Wei TingLaura GutiérrezArun James ThirunavukarasuTing Fang TanDarren Shu Jeng TingJoshua Yi Min TungV. SelladuraiS.R. Devadasan
- Journals
- Nature Medicine (2 papers)Scientific Reports (1 paper)Journal of Medical Internet Research (3 papers)
- Partner nations
- SingaporeUnited StatesUnited Kingdom
In The Last Decade
Kabilan Elangovan
20 papers receiving 1.6k citations
Hit Papers
Peers
Comparison fields: 5 of 159
- Health Informatics 747
- Family Practice 73
- Artificial Intelligence 692
- Health Information Management 96
- Radiology, Nuclear Medicine and Imaging 390
Countries citing papers authored by Kabilan Elangovan
This map shows the geographic impact of Kabilan Elangovan'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 Kabilan Elangovan with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Kabilan Elangovan more than expected).
Fields of papers citing papers by Kabilan Elangovan
This network shows the impact of papers produced by Kabilan Elangovan. 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 Kabilan Elangovan. The network helps show where Kabilan Elangovan may publish in the future.
Co-authorship network
The 25 scholars most cited alongside Kabilan Elangovan, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | 2025 | 1 | |
| 2 | 2025 | 0 | |
| 3 | Retrieval augmented generation for 10 large language models and its generalizability in assessing medical fitnessbreakdown → | 2025 | 30 |
| 4 | 2025 | 0 | |
| 5 | 2025 | 1 | |
| 6 | 2025 | 8 | |
| 7 | 2025 | 1 | |
| 8 | 2024 | 16 | |
| 9 | 2024 | 6 | |
| 10 | 2024 | 1 | |
| 11 | 2024 | 2 | |
| 12 | 2023 | 17 | |
| 13 | Large language models in medicinebreakdown → | 2023 | 1541 |
| 14 | 2023 | 1 | |
| 15 | 2020 | 8 | |
| 16 | 2019 | 2 | |
| 17 | 2019 | 1 | |
| 18 | 2018 | 1 | |
| 19 | 2008 | 1 | |
| 20 | 2006 | 3 |
About Kabilan Elangovan
Kabilan Elangovan is a scholar working on Health Informatics, Family Practice and Medical Laboratory Technology, having authored 23 papers that have together received 1.7k indexed citations. Recurring topics across this work include Artificial Intelligence in Healthcare and Education (12 papers), Machine Learning in Healthcare (5 papers), Radiomics and Machine Learning in Medical Imaging (4 papers), AI in cancer detection (3 papers), Sustainable Supply Chain Management (3 papers), Quality and Management Systems (3 papers), Topic Modeling (2 papers) and Quality and Supply Management (2 papers). The work is most often cited by research in Health Informatics (747 citations), Family Practice (73 citations) and Artificial Intelligence (692 citations). Kabilan Elangovan has collaborated with scholars based in Singapore, United States and United Kingdom. Frequent co-authors include Daniel Shu Wei Ting, Laura Gutiérrez, Arun James Thirunavukarasu, Ting Fang Tan, Darren Shu Jeng Ting, Joshua Yi Min Tung, V. Selladurai, S.R. Devadasan, Hairil Rizal Abdullah and Jasmine Chiat Ling Ong. Their work appears in journals such as Nature Medicine, Scientific Reports and Journal of Medical Internet Research.
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.