Mohan Bhandari
- Health Informatics top 5%
- Artificial Intelligence in Healthcare and Education 3
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- Radiomics and Machine Learning in Medical Imaging 4
- COVID-19 diagnosis using AI 2
- Health Information Management top 10%
- Artificial Intelligence in Healthcare 2
- Artificial Intelligence top 10%
- Explainable Artificial Intelligence (XAI) 3
- Machine Learning in Healthcare 2
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- Currency Recognition and Detection 2
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- Phytoplasmas and Hemiptera pathogens 1
Mohan Bhandari
14 papers receiving 345 citations
Peers
Comparison fields: 5 of 74
- Health Informatics 44
- Neurology 62
- Radiology, Nuclear Medicine and Imaging 133
- Health Information Management 26
- Artificial Intelligence 138
Countries citing papers authored by Mohan Bhandari
This map shows the geographic impact of Mohan Bhandari'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 Mohan Bhandari with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Mohan Bhandari more than expected).
Fields of papers citing papers by Mohan Bhandari
This network shows the impact of papers produced by Mohan Bhandari. 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 Mohan Bhandari. The network helps show where Mohan Bhandari may publish in the future.
Co-authorship network
The 21 scholars most cited alongside Mohan Bhandari, 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 | 2024 | 0 | |
| 3 | 2023 | 1 | |
| 4 | 2023 | 5 | |
| 5 | 2023 | 9 | |
| 6 | 2023 | 35 | |
| 7 | 2023 | 0 | |
| 8 | 2023 | 16 | |
| 9 | 2023 | 38 | |
| 10 | 2023 | 10 | |
| 11 | 2022 | 7 | |
| 12 | 2022 | 100 | |
| 13 | 2022 | 89 | |
| 14 | 2022 | 33 | |
| 15 | 2022 | 14 | |
| 16 | 2020 | 8 |
About Mohan Bhandari
Mohan Bhandari is a scholar working on Health Informatics, Health Information Management, Computer Vision and Pattern Recognition, Radiology, Nuclear Medicine and Imaging and Artificial Intelligence, having authored 16 papers that have together received 366 indexed citations. Recurring topics across this work include Radiomics and Machine Learning in Medical Imaging (4 papers), Explainable Artificial Intelligence (XAI) (3 papers), Artificial Intelligence in Healthcare and Education (3 papers), Machine Learning in Healthcare (2 papers), Artificial Intelligence in Healthcare (2 papers), COVID-19 diagnosis using AI (2 papers), Currency Recognition and Detection (2 papers) and Phytoplasmas and Hemiptera pathogens (1 paper). The work is most often cited by research in Health Informatics (44 citations), Neurology (62 citations), Radiology, Nuclear Medicine and Imaging (133 citations), Health Information Management (26 citations) and Artificial Intelligence (138 citations). Mohan Bhandari has collaborated with scholars based in Nepal, Australia and Malaysia. Frequent co-authors include Arjun Neupane, Tej Bahadur Shahi, Loveleen Gaur, Saurav Mallik, Zhongming Zhao, Kerry B. Walsh, Joan Condell, Pratheepan Yogarajah, Muthu Subash Kavitha and N. Z. Jhanjhi. Their work appears in journals such as Computers in Biology and Medicine, Applied Sciences, Frontiers in Genetics, Physics of the Dark Universe and ACM Transactions on Multimedia Computing Communications and Applications.
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.