Ai Dozen
Impact in
- Health Informatics top 1%
- Artificial Intelligence in Healthcare and Education
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- Radiomics and Machine Learning in Medical Imaging
- COVID-19 diagnosis using AI
- Autopsy Techniques and Outcomes
Papers in
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- Radiomics and Machine Learning in Medical Imaging 2
- Autopsy Techniques and Outcomes 1
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- Ovarian cancer diagnosis and treatment 4
- Co-authors
- Hidenori Machino (11 shared papers)Ken Asada (11 shared papers)Ryuji Hamamoto (11 shared papers)Syuzo Kaneko (11 shared papers)Kanto Shozu (11 shared papers)Masaaki Komatsu (11 shared papers)Akira Sakai (7 shared papers)Suguru Yasutomi (6 shared papers)
- Journals
- Biomedicines (2 papers)Applied Sciences (2 papers)Biomolecules (2 papers)Japanese Journal of Clinical Oncology (1 paper)Clinical Epigenetics (1 paper)
- Partner nations
- JapanUnited KingdomUnited States
In The Last Decade
Ai Dozen
13 papers receiving 507 citations
Peers
Comparison fields: 5 of 84
- Health Informatics 119
- Radiology, Nuclear Medicine and Imaging 193
- Critical Care and Intensive Care Medicine 30
- Health Information Management 23
- Pediatrics, Perinatology and Child Health 83
Countries citing papers authored by Ai Dozen
This map shows the geographic impact of Ai Dozen'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 Ai Dozen with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Ai Dozen more than expected).
Fields of papers citing papers by Ai Dozen
This network shows the impact of papers produced by Ai Dozen. 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 Ai Dozen. The network helps show where Ai Dozen may publish in the future.
Co-authors
The 25 scholars most cited alongside Ai Dozen, 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 | 2020 | 145 | |
| 2 | 2021 | 95 | |
| 3 | 2021 | 83 | |
| 4 | 2020 | 63 | |
| 5 | 2022 | 35 | |
| 6 | 2020 | 34 | |
| 7 | 2021 | 24 | |
| 8 | 2022 | 18 | |
| 9 | 2022 | 11 | |
| 10 | 2022 | 9 | |
| 11 | 2023 | 7 | |
| 12 | 2020 | 2 | |
| 13 | 2019 | 2 |
About Ai Dozen
Ai Dozen is a scholar working on Radiology, Nuclear Medicine and Imaging, Reproductive Medicine, Pediatrics, Perinatology and Child Health, Health Informatics and Molecular Biology, having authored 13 papers that have together received 528 indexed citations. Recurring topics across this work include Ovarian cancer diagnosis and treatment (4 papers), Fetal and Pediatric Neurological Disorders (3 papers), Artificial Intelligence in Healthcare and Education (3 papers), Radiomics and Machine Learning in Medical Imaging (2 papers), Advanced Neural Network Applications (2 papers), Epigenetics and DNA Methylation (1 paper), Autopsy Techniques and Outcomes (1 paper) and Medical Image Segmentation Techniques (1 paper). The work is most often cited by research in Health Informatics (119 citations), Radiology, Nuclear Medicine and Imaging (193 citations), Critical Care and Intensive Care Medicine (30 citations), Health Information Management (23 citations) and Pediatrics, Perinatology and Child Health (83 citations). Ai Dozen has collaborated with scholars based in Japan, United Kingdom and United States. Frequent co-authors include Hidenori Machino, Ken Asada, Ryuji Hamamoto, Syuzo Kaneko, Kanto Shozu, Masaaki Komatsu, Akira Sakai, Suguru Yasutomi, Reina Komatsu and Tatsuya Arakaki. Their work appears in journals such as Biomedicines, Applied Sciences, Biomolecules, Japanese Journal of Clinical Oncology and Clinical Epigenetics.
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.