David Oniani
Impact in
- Health Informatics top 2%
- Artificial Intelligence in Healthcare and Education
- Health Information Management top 10%
Papers in
-
- Machine Learning in Healthcare 7
- Topic Modeling 4
-
- Biomedical Text Mining and Ontologies 4
- Machine Learning in Bioinformatics 2
- Co-authors
- Yanshan Wang (13 shared papers)Gary L. Legault (1 shared paper)Yifan Peng (1 shared paper)Ronald K. Poropatich (1 shared paper)Jeremy Pamplin (1 shared paper)Li-Jia Li (2 shared papers)Bryant Lin (1 shared paper)Iman Azimi (1 shared paper)
- Journals
- npj Digital Medicine (2 papers)Journal of the American Medical Informatics Association (2 papers)Scientific Reports (1 paper)Journal of Clinical Oncology (1 paper)Advances in Nutrition (1 paper)
- Partner nations
- United StatesPolandCanada
In The Last Decade
David Oniani
13 papers receiving 207 citations
Hit Papers
Peers
Comparison fields: 5 of 76
- Health Informatics 74
- Health Information Management 15
- Artificial Intelligence 93
- Safety Research 17
- Applied Psychology 7
Countries citing papers authored by David Oniani
This map shows the geographic impact of David Oniani'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 David Oniani with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites David Oniani more than expected).
Fields of papers citing papers by David Oniani
This network shows the impact of papers produced by David Oniani. 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 David Oniani. The network helps show where David Oniani may publish in the future.
Co-authors
The 25 scholars most cited alongside David Oniani, 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 | 2024 | 72 | |
| 2 | 2023 | 61 | |
| 3 | A Scoping Review of Artificial Intelligence for Precision Nutrition Hit paper breakdown → | 2025 | 18 |
| 4 | 2020 | 16 | |
| 5 | 2024 | 13 | |
| 6 | 2024 | 8 | |
| 7 | 2024 | 7 | |
| 8 | 2023 | 6 | |
| 9 | 2024 | 4 | |
| 10 | 2024 | 3 | |
| 11 | 2023 | 2 | |
| 12 | 2024 | 2 | |
| 13 | 2024 | 2 | |
| 14 | 2023 | 0 |
About David Oniani
David Oniani is a scholar working on Artificial Intelligence, Molecular Biology, Health Informatics, Health Information Management and Computational Theory and Mathematics, having authored 14 papers that have together received 214 indexed citations. Recurring topics across this work include Machine Learning in Healthcare (7 papers), Artificial Intelligence in Healthcare and Education (6 papers), Biomedical Text Mining and Ontologies (4 papers), Topic Modeling (4 papers), Machine Learning in Bioinformatics (2 papers), Artificial Intelligence in Healthcare (2 papers), Computational Drug Discovery Methods (2 papers) and COVID-19 diagnosis using AI (1 paper). The work is most often cited by research in Health Informatics (74 citations), Health Information Management (15 citations), Artificial Intelligence (93 citations), Safety Research (17 citations) and Applied Psychology (7 citations). David Oniani has collaborated with scholars based in United States, Poland and Canada. Frequent co-authors include Yanshan Wang, Gary L. Legault, Yifan Peng, Ronald K. Poropatich, Jeremy Pamplin, Li-Jia Li, Bryant Lin, Iman Azimi, Alexander Thieme and Olivier Gevaert. Their work appears in journals such as npj Digital Medicine, Journal of the American Medical Informatics Association, Scientific Reports, Journal of Clinical Oncology and Advances in Nutrition.
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.