Daiju Ueda

3.8k total citations · 2 hit papers
123 papers, 2.3k citations indexed

About

Daiju Ueda is a scholar working on Radiology, Nuclear Medicine and Imaging, Electrical and Electronic Engineering and Condensed Matter Physics. According to data from OpenAlex, Daiju Ueda has authored 123 papers receiving a total of 2.3k indexed citations (citations by other indexed papers that have themselves been cited), including 54 papers in Radiology, Nuclear Medicine and Imaging, 42 papers in Electrical and Electronic Engineering and 28 papers in Condensed Matter Physics. Recurrent topics in Daiju Ueda's work include Radiomics and Machine Learning in Medical Imaging (34 papers), GaN-based semiconductor devices and materials (28 papers) and Artificial Intelligence in Healthcare and Education (23 papers). Daiju Ueda is often cited by papers focused on Radiomics and Machine Learning in Medical Imaging (34 papers), GaN-based semiconductor devices and materials (28 papers) and Artificial Intelligence in Healthcare and Education (23 papers). Daiju Ueda collaborates with scholars based in Japan, United States and Italy. Daiju Ueda's 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 and has published in prestigious journals such as SHILAP Revista de lepidopterología, Applied Physics Letters and PLoS ONE.

In The Last Decade

Daiju Ueda

109 papers receiving 2.2k citations

Hit Papers

align trajectories

What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

Fairness of artificial intelligence in healthcare: review and recommendations

2023 236 citations Daiju Ueda, Shohei Fujita et al. Japanese Journal of Radiology profile →
A systematic review and meta-analysis of diagnostic performance comparison between generative AI and physicians

2025 28 citations Hirotaka Takita, Daijiro Kabata et al. profile →

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name	h						Papers	Cites
Daiju Ueda Japan	26	838	661	620	378	360	123	2.3k
Akira Sakai Japan	25	265 0.3×	785 1.2×	134 0.2×	171 0.5×	63 0.2×	161	2.2k
Daniel Kim United States	33	3.0k 3.6×	259 0.4×	43 0.1×	183 0.5×	26 0.1×	134	4.6k
Masahiro Oda Japan	26	913 1.1×	28 0.0×	50 0.1×	372 1.0×	1.1k 3.0×	180	3.3k
Leonard Wee Netherlands	25	1.9k 2.3×	17 0.0×	182 0.3×	466 1.2×	96 0.3×	127	2.5k
Akihiro Haga Japan	15	558 0.7×	106 0.2×	73 0.1×	88 0.2×	14 0.0×	105	1.1k
C. Y. Hu China	22	446 0.5×	393 0.6×	19 0.0×	977 2.6×	41 0.1×	52	1.9k
Shih-Cheng Huang United States	10	589 0.7×	29 0.0×	177 0.3×	535 1.4×	50 0.1×	22	1.3k
Aldo Badano United States	26	1.8k 2.1×	168 0.3×	45 0.1×	416 1.1×	17 0.0×	222	2.9k
Jimmy Wang United States	16	209 0.2×	200 0.3×	45 0.1×	198 0.5×	51 0.1×	50	1.0k
Yueh Z. Lee United States	26	966 1.2×	76 0.1×	49 0.1×	56 0.1×	10 0.0×	162	2.3k

Countries citing papers authored by Daiju Ueda

Since Specialization

Citations

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

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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 of co-authors of Daiju Ueda

This figure shows the co-authorship network connecting the top 25 collaborators of Daiju Ueda. A scholar is included among the top collaborators of Daiju Ueda based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Daiju Ueda. Daiju Ueda is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

Sort: Min cites: Since: Top N: Style:

20 of 20 papers shown

Hatabu, Hiroto, Masahiro Yanagawa, Yoshitake Yamada, et al.. (2025). Recent trends in scientific research in chest radiology: What to do or not to do? That is the critical question in research. Japanese Journal of Radiology. 43(6). 883–902. 1 indexed citations

Honda, Maya, Mami Iima, Masako Kataoka, et al.. (2025). Breast cancer imaging without gadolinium-based contrast agent: A review of current applications and future trends. Diagnostic and Interventional Imaging. 107(2). 51–61.

Kurokawa, Ryo, Akifumi Hagiwara, Rintaro Ito, et al.. (2025). Illuminating radiogenomic signatures in pediatric-type diffuse gliomas: insights into molecular, clinical, and imaging correlations. Part II: low-grade group. La radiologia medica. 130(9). 1503–1515.

Kurokawa, Ryo, Akifumi Hagiwara, Daiju Ueda, et al.. (2025). Illuminating radiogenomic signatures in pediatric-type diffuse gliomas: insights into molecular, clinical, and imaging correlations. Part I: high-grade group. La radiologia medica. 130(11). 1871–1887.

Yamamoto, Akira, Shingo Sato, Daiju Ueda, et al.. (2025). Deep-learning-based prediction of significant portal hypertension with single cross-sectional non-enhanced CT. European Radiology. 36(3). 1899–1908.

Shinkawa, Hiroji, Daiju Ueda, Masaki Kaibori, et al.. (2025). Individualized Prognostication Based on Deep‐Learning Models Using Computed Tomography as an Imaging Biomarker After Hepatocellular Carcinoma Resection. Hepatology Research. 56(2). 204–213.

Nozaki, Taiki, Masahiro Hashimoto, Daiju Ueda, et al.. (2025). Recent topics in musculoskeletal imaging focused on clinical applications of AI: How should radiologists approach and use AI?. La radiologia medica. 130(5). 587–597. 1 indexed citations

Ueda, Daiju, Toshimasa Matsumoto, Akira Yamamoto, et al.. (2024). A deep learning-based model to estimate pulmonary function from chest x-rays: multi-institutional model development and validation study in Japan. The Lancet Digital Health. 6(8). e580–e588. 4 indexed citations

Ueda, Daiju, Shannon L. Walston, Shohei Fujita, et al.. (2024). Climate change and artificial intelligence in healthcare: Review and recommendations towards a sustainable future. Diagnostic and Interventional Imaging. 105(11). 453–459. 27 indexed citations

10.

Takita, Hirotaka, Daijiro Kabata, Shannon L. Walston, et al.. (2024). Diagnostic Performance Comparison between Generative AI and Physicians: A Systematic Review and Meta-Analysis. medRxiv. 18 indexed citations

11.

Matsui, Yusuke, Daiju Ueda, Shohei Fujita, et al.. (2024). Applications of artificial intelligence in interventional oncology: An up-to-date review of the literature. Japanese Journal of Radiology. 43(2). 164–176. 7 indexed citations

12.

Ueda, Daiju, Shannon L. Walston, Hirotaka Takita, Yasuhito Mitsuyama, & Yukio Miki. (2024). The critical need for an open medical imaging database in Japan: implications for global health and AI development. Japanese Journal of Radiology. 43(4). 537–541. 1 indexed citations

13.

Horiuchi, Daisuke, Hiroyuki Tatekawa, Shannon L. Walston, et al.. (2024). Comparing the Diagnostic Performance of GPT-4-based ChatGPT, GPT-4V-based ChatGPT, and Radiologists in Challenging Neuroradiology Cases. Clinical Neuroradiology. 34(4). 779–787. 32 indexed citations

14.

Kinoshita, Masahiko, Daiju Ueda, Toshimasa Matsumoto, et al.. (2023). Deep Learning Model Based on Contrast-Enhanced Computed Tomography Imaging to Predict Postoperative Early Recurrence after the Curative Resection of a Solitary Hepatocellular Carcinoma. Cancers. 15(7). 2140–2140. 17 indexed citations

15.

Tatsugami, Fuminari, Takeshi Nakaura, Masahiro Yanagawa, et al.. (2023). Recent advances in artificial intelligence for cardiac CT: Enhancing diagnosis and prognosis prediction. Diagnostic and Interventional Imaging. 104(11). 521–528. 28 indexed citations

16.

Yamada, Akira, Koji Kamagata, Kenji Hirata, et al.. (2023). Clinical applications of artificial intelligence in liver imaging. La radiologia medica. 128(6). 655–667. 23 indexed citations

17.

Fujima, Noriyuki, Koji Kamagata, Daiju Ueda, et al.. (2023). Current State of Artificial Intelligence in Clinical Applications for Head and Neck MR Imaging. Magnetic Resonance in Medical Sciences. 22(4). 401–414. 14 indexed citations

18.

Hirata, Kenji, Koji Kamagata, Daiju Ueda, et al.. (2023). From FDG and beyond: the evolving potential of nuclear medicine. Annals of Nuclear Medicine. 37(11). 583–595. 15 indexed citations

19.

Mitsuyama, Yasuhito, Toshimasa Matsumoto, Hiroyuki Tatekawa, et al.. (2023). Chest radiography as a biomarker of ageing: artificial intelligence-based, multi-institutional model development and validation in Japan. The Lancet Healthy Longevity. 4(9). e478–e486. 14 indexed citations

20.

Yoshida, Atsushi, Daiju Ueda, Shigeaki Higashiyama, et al.. (2022). Deep learning-based detection of parathyroid adenoma by 99mTc-MIBI scintigraphy in patients with primary hyperparathyroidism. Annals of Nuclear Medicine. 36(5). 468–478. 10 indexed citations

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.

Explore authors with similar magnitude of impact