Nishanth Arun
Impact in
- Health Informatics top 2%
- Artificial Intelligence in Healthcare and Education
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- COVID-19 diagnosis using AI
- Radiomics and Machine Learning in Medical Imaging
Papers in
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- COVID-19 diagnosis using AI 3
- Optical Imaging and Spectroscopy Techniques 1
- Retinal Imaging and Analysis 1
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- Lung Cancer Diagnosis and Treatment 1
- Phonocardiography and Auscultation Techniques 1
- Digital Radiography and Breast Imaging 1
- Co-authors
- Jayashree Kalpathy–Cramer (5 shared papers)Ken Chang (3 shared papers)Praveer Singh (5 shared papers)Matthew Li (3 shared papers)Mishka Gidwani (2 shared papers)Nathan Gaw (2 shared papers)Katharina Hoebel (2 shared papers)Mehak Aggarwal (2 shared papers)
- Journals
- Radiology Artificial Intelligence (2 papers)Investigative Ophthalmology & Visual Science (1 paper)Medicine (1 paper)Journal of the American College of Radiology (1 paper)
- Partner nations
- United StatesIndiaBrazil
In The Last Decade
Nishanth Arun
5 papers receiving 295 citations
Peers
Comparison fields: 5 of 62
- Health Informatics 79
- Radiology, Nuclear Medicine and Imaging 209
- Artificial Intelligence 125
- Biophysics 9
- Pulmonary and Respiratory Medicine 51
Countries citing papers authored by Nishanth Arun
This map shows the geographic impact of Nishanth Arun'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 Nishanth Arun with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Nishanth Arun more than expected).
Fields of papers citing papers by Nishanth Arun
This network shows the impact of papers produced by Nishanth Arun. 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 Nishanth Arun. The network helps show where Nishanth Arun may publish in the future.
Co-authors
The 25 scholars most cited alongside Nishanth Arun, 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 | 2021 | 147 | |
| 2 | 2020 | 105 | |
| 3 | 2020 | 37 | |
| 4 | 2022 | 10 | |
| 5 | Automated detection of genetic relatedness from fundus photographs using Convolutional Siamese Neural Networks | 2021 | 1 |
| 6 | 2025 | 0 | |
| 7 | 2025 | 0 |
About Nishanth Arun
Nishanth Arun is a scholar working on Radiology, Nuclear Medicine and Imaging, Pulmonary and Respiratory Medicine, Artificial Intelligence, Infectious Diseases and Computer Networks and Communications, having authored 7 papers that have together received 300 indexed citations. Recurring topics across this work include COVID-19 diagnosis using AI (3 papers), AI in cancer detection (2 papers), Optical Imaging and Spectroscopy Techniques (1 paper), Retinal Imaging and Analysis (1 paper), Lung Cancer Diagnosis and Treatment (1 paper), Advanced Malware Detection Techniques (1 paper), Phonocardiography and Auscultation Techniques (1 paper) and Digital Radiography and Breast Imaging (1 paper). The work is most often cited by research in Health Informatics (79 citations), Radiology, Nuclear Medicine and Imaging (209 citations), Artificial Intelligence (125 citations), Biophysics (9 citations) and Pulmonary and Respiratory Medicine (51 citations). Nishanth Arun has collaborated with scholars based in United States, India and Brazil. Frequent co-authors include Jayashree Kalpathy–Cramer, Ken Chang, Praveer Singh, Matthew Li, Mishka Gidwani, Nathan Gaw, Katharina Hoebel, Mehak Aggarwal, Sharut Gupta and Julius Adebayo. Their work appears in journals such as Radiology Artificial Intelligence, Investigative Ophthalmology & Visual Science, Medicine and Journal of the American College of Radiology.
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.