Daiju Ueda
- Health Informatics top 0.05%
- Artificial Intelligence in Healthcare and Education 23
-
- Radiomics and Machine Learning in Medical Imaging 34
- COVID-19 diagnosis using AI 17
- Condensed Matter Physics top 5%
- GaN-based semiconductor devices and materials 28
- Artificial Intelligence top 5%
- Family Practice top 10%
-
- Semiconductor materials and devices 23
- Advancements in Semiconductor Devices and Circuit Design 16
- Silicon Carbide Semiconductor Technologies 12
-
- Semiconductor Quantum Structures and Devices 17
- Co-authors
- Yukio MikiShannon L. WalstonAkitoshi ShimazakiHiromitsu TakagiG. KanoAkira YamamotoHiroyuki TatekawaHirotaka Takita
- Journals
- SHILAP Revista de lepidopterología (1 paper)Applied Physics Letters (3 papers)PLoS ONE (1 paper)
- Partner nations
- JapanUnited StatesItaly
In The Last Decade
Daiju Ueda
109 papers receiving 2.2k citations
Hit Papers
Peers
Comparison fields: 5 of 127
- Health Informatics 620
- Radiology, Nuclear Medicine and Imaging 838
- Condensed Matter Physics 360
- Artificial Intelligence 378
- Family Practice 26
Countries citing papers authored by Daiju Ueda
This map shows the geographic impact of Daiju Ueda'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 Daiju Ueda with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Daiju Ueda more than expected).
Fields of papers citing papers by Daiju Ueda
This network shows the impact of papers produced by Daiju Ueda. 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 Daiju Ueda. The network helps show where Daiju Ueda may publish in the future.
Co-authorship network
The 25 scholars most cited alongside Daiju Ueda, 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 | 2025 | 0 | |
| 3 | 2025 | 0 | |
| 4 | 2025 | 0 | |
| 5 | 2025 | 0 | |
| 6 | 2025 | 0 | |
| 7 | 2025 | 1 | |
| 8 | 2024 | 4 | |
| 9 | 2024 | 27 | |
| 10 | 2024 | 18 | |
| 11 | 2024 | 7 | |
| 12 | 2024 | 1 | |
| 13 | 2024 | 32 | |
| 14 | 2023 | 17 | |
| 15 | 2023 | 28 | |
| 16 | 2023 | 23 | |
| 17 | 2023 | 14 | |
| 18 | 2023 | 15 | |
| 19 | 2023 | 14 | |
| 20 | 2022 | 10 |
About Daiju Ueda
Daiju Ueda is a scholar working on Health Informatics, Condensed Matter Physics and Radiology, Nuclear Medicine and Imaging, having authored 123 papers that have together received 2.3k indexed citations. Recurring topics across this work include Radiomics and Machine Learning in Medical Imaging (34 papers), GaN-based semiconductor devices and materials (28 papers), Artificial Intelligence in Healthcare and Education (23 papers), Semiconductor materials and devices (23 papers), Semiconductor Quantum Structures and Devices (17 papers), COVID-19 diagnosis using AI (17 papers), Advancements in Semiconductor Devices and Circuit Design (16 papers) and Silicon Carbide Semiconductor Technologies (12 papers). The work is most often cited by research in Health Informatics (620 citations), Radiology, Nuclear Medicine and Imaging (838 citations) and Condensed Matter Physics (360 citations). Daiju Ueda has collaborated with scholars based in Japan, United States and Italy. Frequent co-authors include Yukio Miki, Shannon L. Walston, Akitoshi Shimazaki, Hiromitsu Takagi, G. Kano, Akira Yamamoto, Hiroyuki Tatekawa, Hirotaka Takita, Yasuhito Mitsuyama and Taro Shimono. Their work appears in journals such as SHILAP Revista de lepidopterología, Applied Physics Letters and PLoS ONE.
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.