Arjun Panesar
Impact in
- Health Informatics top 5%
- Artificial Intelligence in Healthcare and Education
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- Artificial Intelligence in Healthcare
Papers in
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- Mobile Health and mHealth Applications 5
- Health Literacy and Information Accessibility 1
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- Eating Disorders and Behaviors 1
- Co-authors
- Amit Kaura (2 shared papers)Philip Fei Wu (2 shared papers)Charlotte Summers (2 shared papers)Li Zhang (2 shared papers)Jonathan Cave (1 shared paper)Frances Griffiths (1 shared paper)Ashish Suttee (1 shared paper)Emma Scott (1 shared paper)
- Journals
- Knowledge-Based Systems (1 paper)BJGP Open (1 paper)JMIR Formative Research (2 papers)JMIR Human Factors (2 papers)JMIR Diabetes (1 paper)
- Partner nations
- United KingdomAustraliaFrance
In The Last Decade
Arjun Panesar
10 papers receiving 169 citations
Peers
Comparison fields: 5 of 83
- Health Informatics 42
- Health Information Management 36
- Medical Laboratory Technology 3
- Applied Psychology 10
- Artificial Intelligence 49
Countries citing papers authored by Arjun Panesar
This map shows the geographic impact of Arjun Panesar'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 Arjun Panesar with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Arjun Panesar more than expected).
Fields of papers citing papers by Arjun Panesar
This network shows the impact of papers produced by Arjun Panesar. 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 Arjun Panesar. The network helps show where Arjun Panesar may publish in the future.
Co-authors
The 18 scholars most cited alongside Arjun Panesar, 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 | 2019 | 77 | |
| 2 | 2020 | 54 | |
| 3 | 2019 | 21 | |
| 4 | 2024 | 13 | |
| 5 | 2022 | 5 | |
| 6 | 2023 | 4 | |
| 7 | 2021 | 3 | |
| 8 | 2025 | 2 | |
| 9 | 2023 | 1 | |
| 10 | 2021 | 1 | |
| 11 | 2023 | 0 | |
| 12 | 2024 | 0 | |
| 13 | 2022 | 0 |
About Arjun Panesar
Arjun Panesar is a scholar working on General Health Professions, Clinical Psychology, Epidemiology, Artificial Intelligence and Physiology, having authored 13 papers that have together received 181 indexed citations. Recurring topics across this work include Mobile Health and mHealth Applications (5 papers), Speech and Audio Processing (1 paper), Diabetes Management and Research (1 paper), Health Literacy and Information Accessibility (1 paper), Obesity and Health Practices (1 paper), Artificial Intelligence in Healthcare (1 paper), Neural Networks and Applications (1 paper) and Eating Disorders and Behaviors (1 paper). The work is most often cited by research in Health Informatics (42 citations), Health Information Management (36 citations), Medical Laboratory Technology (3 citations), Applied Psychology (10 citations) and Artificial Intelligence (49 citations). Arjun Panesar has collaborated with scholars based in United Kingdom, Australia and France. Frequent co-authors include Amit Kaura, Philip Fei Wu, Charlotte Summers, Li Zhang, Jonathan Cave, Frances Griffiths, Ashish Suttee, Emma Scott, Jeremy Dale and Houshyar Asadi. Their work appears in journals such as Knowledge-Based Systems, BJGP Open, JMIR Formative Research, JMIR Human Factors and JMIR Diabetes.
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.