Felipe Kitamura

3.7k total citations
42 papers, 854 citations indexed

About

Felipe Kitamura is a scholar working on Radiology, Nuclear Medicine and Imaging, Health Informatics and Artificial Intelligence. According to data from OpenAlex, Felipe Kitamura has authored 42 papers receiving a total of 854 indexed citations (citations by other indexed papers that have themselves been cited), including 30 papers in Radiology, Nuclear Medicine and Imaging, 22 papers in Health Informatics and 10 papers in Artificial Intelligence. Recurrent topics in Felipe Kitamura's work include Artificial Intelligence in Healthcare and Education (22 papers), Radiomics and Machine Learning in Medical Imaging (20 papers) and AI in cancer detection (8 papers). Felipe Kitamura is often cited by papers focused on Artificial Intelligence in Healthcare and Education (22 papers), Radiomics and Machine Learning in Medical Imaging (20 papers) and AI in cancer detection (8 papers). Felipe Kitamura collaborates with scholars based in Brazil, United States and Canada. Felipe Kitamura's co-authors include Bradley J. Erickson, Ian Pan, Nitamar Abdala, Luciano M. Prevedello, Katherine P. Andriole, Adam E. Flanders, Jayashree Kalpathy–Cramer, Lucas Pereira, Rafael T. Sousa and D. Cheng and has published in prestigious journals such as PLoS ONE, Stroke and Scientific Reports.

In The Last Decade

Felipe Kitamura

39 papers receiving 834 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Felipe Kitamura Brazil 13 413 284 232 142 120 42 854
Shahein Tajmir United States 13 505 1.2× 271 1.0× 222 1.0× 244 1.7× 174 1.4× 19 1.2k
Sehyo Yune South Korea 11 343 0.8× 162 0.6× 181 0.8× 118 0.8× 42 0.3× 22 778
Shankeeth Vinayahalingam Netherlands 14 179 0.4× 118 0.4× 127 0.5× 221 1.6× 36 0.3× 55 719
Byoung-Dai Lee South Korea 12 200 0.5× 49 0.2× 60 0.3× 106 0.7× 82 0.7× 51 501
Brent J. Liu United States 10 190 0.5× 28 0.1× 71 0.3× 85 0.6× 165 1.4× 74 550
Jeroen Bertels Belgium 10 210 0.5× 23 0.1× 116 0.5× 114 0.8× 67 0.6× 15 527
Christian Payer Austria 11 169 0.4× 28 0.1× 71 0.3× 213 1.5× 123 1.0× 20 640
Darko Štern Austria 17 221 0.5× 49 0.2× 103 0.4× 407 2.9× 210 1.8× 34 960
Muthu Subash Kavitha Japan 15 211 0.5× 40 0.1× 190 0.8× 217 1.5× 16 0.1× 76 835
Katy Blumer United States 6 838 2.0× 215 0.8× 328 1.4× 171 1.2× 6 0.1× 8 1.5k

Countries citing papers authored by Felipe Kitamura

Since Specialization
Citations

This map shows the geographic impact of Felipe Kitamura'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 Felipe Kitamura with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Felipe Kitamura more than expected).

Fields of papers citing papers by Felipe Kitamura

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Felipe Kitamura. 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 Felipe Kitamura. The network helps show where Felipe Kitamura may publish in the future.

Co-authorship network of co-authors of Felipe Kitamura

This figure shows the co-authorship network connecting the top 25 collaborators of Felipe Kitamura. A scholar is included among the top collaborators of Felipe Kitamura 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 Felipe Kitamura. Felipe Kitamura is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Yi, Paul H., Hana L. Haver, Jean Jeudy, et al.. (2025). Best Practices for the Safe Use of Large Language Models and Other Generative AI in Radiology. Radiology. 316(3). e241516–e241516. 1 indexed citations
2.
Grassi, Francesca, Heron Werner, Felipe Kitamura, et al.. (2025). Current applications and future perspectives of extended reality in radiology. La radiologia medica. 130(6). 905–920. 4 indexed citations
3.
Amin, Rada, et al.. (2025). Digital Twin Technology In Radiology. Journal of Imaging Informatics in Medicine. 3 indexed citations
4.
Chen, Yan, Robyn L. Ball, Hari Trivedi, et al.. (2025). Performance of Algorithms Submitted in the 2023 RSNA Screening Mammography Breast Cancer Detection AI Challenge. Radiology. 316(2). e241447–e241447. 1 indexed citations
5.
Kline, Timothy L., Felipe Kitamura, Ian Pan, et al.. (2025). Best Practices and Checklist for Reviewing Artificial Intelligence-Based Medical Imaging Papers: Classification. Journal of Imaging Informatics in Medicine. 2 indexed citations
6.
Maleki, Farhad, Linda Moy, Reza Forghani, et al.. (2024). RIDGE: Reproducibility, Integrity, Dependability, Generalizability, and Efficiency Assessment of Medical Image Segmentation Models. Journal of Imaging Informatics in Medicine. 38(4). 2524–2536. 2 indexed citations
7.
Kitamura, Felipe, Luciano M. Prevedello, Errol Colak, et al.. (2024). Lessons Learned in Building Expertly Annotated Multi-Institution Datasets and Hosting the RSNA AI Challenges. Radiology Artificial Intelligence. 6(3). e230227–e230227. 6 indexed citations
8.
Moassefi, Mana, Yashbir Singh, Gian Marco Conte, et al.. (2024). Checklist for Reproducibility of Deep Learning in Medical Imaging. Journal of Imaging Informatics in Medicine. 37(4). 1664–1673. 6 indexed citations
9.
Lee, Ghee Rye, Adam E. Flanders, Felipe Kitamura, et al.. (2024). Performance of the Winning Algorithms of the RSNA 2022 Cervical Spine Fracture Detection Challenge. Radiology Artificial Intelligence. 6(1). e230256–e230256. 4 indexed citations
10.
Macruz, Fabíola, Mariana Penteado Nucci, Carolina de Medeiros Rimkus, et al.. (2024). The new era of artificial intelligence in neuroradiology: current research and promising tools. Arquivos de Neuro-Psiquiatria. 82(6). 1–12.
11.
Kitamura, Felipe, et al.. (2023). Validation of a deep learning algorithm for bone age estimation among patients in the city of São Paulo, Brazil. Radiologia Brasileira. 56(5). 263–268. 1 indexed citations
12.
Sreekrishnan, Anirudh, Dan‐Victor Giurgiutiu, Felipe Kitamura, et al.. (2023). Decreasing false-positive detection of intracranial hemorrhage (ICH) using RAPID ICH 3. Journal of Stroke and Cerebrovascular Diseases. 32(12). 107396–107396. 3 indexed citations
13.
Moassefi, Mana, Pouria Rouzrokh, Gian Marco Conte, et al.. (2023). Reproducibility of Deep Learning Algorithms Developed for Medical Imaging Analysis: A Systematic Review. Journal of Digital Imaging. 36(5). 2306–2312. 11 indexed citations
14.
Gaube, Susanne, Harini Suresh, Martina Raue, et al.. (2023). Non-task expert physicians benefit from correct explainable AI advice when reviewing X-rays. Scientific Reports. 13(1). 1383–1383. 43 indexed citations
15.
Sreekrishnan, Anirudh, Dan‐Victor Giurgiutiu, Felipe Kitamura, et al.. (2023). Abstract WMP60: Eliminating False Positive Detections Of Intracranial Hemorrhage (ICH) Using RAPID ICH 3. Stroke. 54(Suppl_1).
16.
Li, Matthew, Nishanth Arun, Mehak Aggarwal, et al.. (2022). Multi-population generalizability of a deep learning-based chest radiograph severity score for COVID-19. Medicine. 101(29). e29587–e29587. 10 indexed citations
17.
Kitamura, Felipe, Guillermo Elizondo‐Riojas, Hernán Chaves, et al.. (2022). Forging Connections in Latin America to Advance AI in Radiology. Radiology Artificial Intelligence. 4(5). e220125–e220125. 3 indexed citations
18.
Bridge, Christopher P., Lina Chen, Felipe Kitamura, et al.. (2020). Using DICOM Metadata for Radiological Image Series Categorization: a Feasibility Study on Large Clinical Brain MRI Datasets. Journal of Digital Imaging. 33(3). 747–762. 21 indexed citations
19.
Santos, Fernanda Fernandes, Denise Yamamoto, Cecília M. Abe, et al.. (2019). The Type III Secretion System (T3SS)-Translocon of Atypical Enteropathogenic Escherichia coli (aEPEC) Can Mediate Adherence. Frontiers in Microbiology. 10. 1527–1527. 17 indexed citations
20.
Halabi, Safwan S., Luciano M. Prevedello, Jayashree Kalpathy–Cramer, et al.. (2018). The RSNA Pediatric Bone Age Machine Learning Challenge. Radiology. 290(2). 498–503. 283 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.

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