Jun Kuroda
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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- Artificial Intelligence in Healthcare 4
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- Speech and Audio Processing 3
- Co-authors
- Michiharu Kudo (4 shared papers)Akira Koseki (4 shared papers)Masaki Ono (4 shared papers)Masaki Makino (4 shared papers)Atsushi Suzuki (4 shared papers)Toshinari Itoko (2 shared papers)Ryo Yoshimoto (1 shared paper)Yukio Yuzawa (1 shared paper)
- Journals
- Journal of The Electrochemical Society (2 papers)Scientific Reports (1 paper)Clinical and Experimental Gastroenterology (1 paper)Diabetes (1 paper)Journal of Mass Spectrometry (1 paper)
- Partner nations
- JapanUnited States
In The Last Decade
Jun Kuroda
17 papers receiving 383 citations
Peers
Comparison fields: 5 of 84
- Health Informatics 29
- Health Information Management 64
- Toxicology 20
- Nephrology 37
- Artificial Intelligence 58
Countries citing papers authored by Jun Kuroda
This map shows the geographic impact of Jun Kuroda'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 Jun Kuroda with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Jun Kuroda more than expected).
Fields of papers citing papers by Jun Kuroda
This network shows the impact of papers produced by Jun Kuroda. 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 Jun Kuroda. The network helps show where Jun Kuroda may publish in the future.
Co-authors
The 25 scholars most cited alongside Jun Kuroda, 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 | 171 | |
| 2 | 2013 | 91 | |
| 3 | Cytotoxic and DNA damage-inducing activities of low molecular weight phenols from rhubarb. | 2001 | 67 |
| 4 | 2018 | 13 | |
| 5 | 2019 | 12 | |
| 6 | 2020 | 7 | |
| 7 | 2018 | 5 | |
| 8 | 2015 | 4 | |
| 9 | 2020 | 4 | |
| 10 | 2017 | 4 | |
| 11 | 1998 | 3 | |
| 12 | 2018 | 2 | |
| 13 | Feature Extraction from Electronic Health Records of Diabetic Nephropathy Patients with Convolutioinal Autoencoder. | 2018 | 2 |
| 14 | 2018 | 2 | |
| 15 | 2025 | 2 | |
| 16 | 2022 | 1 | |
| 17 | 2006 | 1 | |
| 18 | 2001 | 1 |
About Jun Kuroda
Jun Kuroda is a scholar working on Health Information Management, Signal Processing, Biomedical Engineering, Health Informatics and Electrical and Electronic Engineering, having authored 18 papers that have together received 392 indexed citations. Recurring topics across this work include Artificial Intelligence in Healthcare (4 papers), Acoustic Wave Phenomena Research (3 papers), Speech and Audio Processing (3 papers), Machine Learning in Healthcare (3 papers), Fuel Cells and Related Materials (1 paper), Engineering Applied Research (1 paper), Electrocatalysts for Energy Conversion (1 paper) and Gallbladder and Bile Duct Disorders (1 paper). The work is most often cited by research in Health Informatics (29 citations), Health Information Management (64 citations), Toxicology (20 citations), Nephrology (37 citations) and Artificial Intelligence (58 citations). Jun Kuroda has collaborated with scholars based in Japan and United States. Frequent co-authors include Michiharu Kudo, Akira Koseki, Masaki Ono, Masaki Makino, Atsushi Suzuki, Toshinari Itoko, Ryo Yoshimoto, Yukio Yuzawa, Kiyotaka Hoshinaga and Eiichi Saitoh. Their work appears in journals such as Journal of The Electrochemical Society, Scientific Reports, Clinical and Experimental Gastroenterology, Diabetes and Journal of Mass Spectrometry.
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.