Jason Cai

913 total citations
21 papers, 576 citations indexed

About

Jason Cai is a scholar working on Artificial Intelligence, Radiology, Nuclear Medicine and Imaging and Pulmonary and Respiratory Medicine. According to data from OpenAlex, Jason Cai has authored 21 papers receiving a total of 576 indexed citations (citations by other indexed papers that have themselves been cited), including 8 papers in Artificial Intelligence, 6 papers in Radiology, Nuclear Medicine and Imaging and 5 papers in Pulmonary and Respiratory Medicine. Recurrent topics in Jason Cai's work include Radiomics and Machine Learning in Medical Imaging (5 papers), Topic Modeling (5 papers) and Natural Language Processing Techniques (5 papers). Jason Cai is often cited by papers focused on Radiomics and Machine Learning in Medical Imaging (5 papers), Topic Modeling (5 papers) and Natural Language Processing Techniques (5 papers). Jason Cai collaborates with scholars based in United States, Thailand and Japan. Jason Cai's co-authors include Bradley J. Erickson, Kenneth A. Philbrick, Alexander D. Weston, Zeynettin Akkus, Arunnit Boonrod, Atefeh Zeinoddini, Pouria Rouzrokh, Cody C. Wyles, Hilal Maradit Kremers and Taghi Ramazanian and has published in prestigious journals such as SHILAP Revista de lepidopterología, PLoS ONE and Radiology.

In The Last Decade

Jason Cai

15 papers receiving 565 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Jason Cai United States 9 219 179 107 96 91 21 576
Kyong Joon Lee South Korea 14 217 1.0× 128 0.7× 88 0.8× 80 0.8× 57 0.6× 40 575
Tsung‐Ying Ho Taiwan 12 303 1.4× 161 0.9× 137 1.3× 60 0.6× 110 1.2× 24 592
Hwiyoung Kim South Korea 18 466 2.1× 67 0.4× 215 2.0× 98 1.0× 103 1.1× 42 902
Nicolai Oetter Germany 11 131 0.6× 152 0.8× 48 0.4× 135 1.4× 22 0.2× 23 483
Beomseok Sohn South Korea 11 257 1.2× 91 0.5× 79 0.7× 30 0.3× 29 0.3× 39 499
P. Meyer France 16 394 1.8× 286 1.6× 135 1.3× 116 1.2× 42 0.5× 65 965
Jiliang Ren China 14 363 1.7× 69 0.4× 65 0.6× 75 0.8× 35 0.4× 27 516
Min Kyoung Lee South Korea 16 189 0.9× 180 1.0× 44 0.4× 35 0.4× 25 0.3× 48 618
Fayu Liu China 14 143 0.7× 337 1.9× 45 0.4× 117 1.2× 17 0.2× 34 671
Barbaros S. Erdal United States 13 281 1.3× 74 0.4× 113 1.1× 127 1.3× 111 1.2× 30 603

Countries citing papers authored by Jason Cai

Since Specialization
Citations

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

Fields of papers citing papers by Jason Cai

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Jason Cai

This figure shows the co-authorship network connecting the top 25 collaborators of Jason Cai. A scholar is included among the top collaborators of Jason Cai 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 Jason Cai. Jason Cai 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.
Ge, Yubin, Salvatore Romeo, Jason Cai, et al.. (2025). TReMu: Towards Neuro-Symbolic Temporal Reasoning for LLM-Agents with Memory in Multi-Session Dialogues. 18974–18988.
2.
Cai, Jason, et al.. (2025). MemInsight: Autonomous Memory Augmentation for LLM Agents. 33124–33140.
3.
4.
5.
Kuanar, Shiba, Jason Cai, Hiroaki Takahashi, et al.. (2024). Transition-zone PSA-density calculated from MRI deep learning prostate zonal segmentation model for prediction of clinically significant prostate cancer. Abdominal Radiology. 49(10). 3722–3734. 2 indexed citations
6.
Song, Hwanjun, et al.. (2024). FineSurE: Fine-grained Summarization Evaluation using LLMs. 906–922. 8 indexed citations
7.
Cai, Jason, Shiba Kuanar, Adam T. Froemming, et al.. (2024). Fully Automated Deep Learning Model to Detect Clinically Significant Prostate Cancer at MRI. Radiology. 312(2). e232635–e232635. 20 indexed citations
8.
9.
Takahashi, Hiroaki, Kotaro Yoshida, Akira Kawashima, et al.. (2022). Impact of measurement method on interobserver variability of apparent diffusion coefficient of lesions in prostate MRI. PLoS ONE. 17(5). e0268829–e0268829. 3 indexed citations
10.
Rouzrokh, Pouria, Cody C. Wyles, Taghi Ramazanian, et al.. (2022). Deep Learning for Radiographic Measurement of Femoral Component Subsidence Following Total Hip Arthroplasty. Radiology Artificial Intelligence. 4(3). e210206–e210206. 17 indexed citations
11.
Rouzrokh, Pouria, Taghi Ramazanian, Cody C. Wyles, et al.. (2021). Deep Learning Artificial Intelligence Model for Assessment of Hip Dislocation Risk Following Primary Total Hip Arthroplasty From Postoperative Radiographs. The Journal of Arthroplasty. 36(6). 2197–2203.e3. 48 indexed citations
12.
Rouzrokh, Pouria, Cody C. Wyles, Kenneth A. Philbrick, et al.. (2021). A Deep Learning Tool for Automated Radiographic Measurement of Acetabular Component Inclination and Version After Total Hip Arthroplasty. The Journal of Arthroplasty. 36(7). 2510–2517.e6. 70 indexed citations
13.
Conte, Gian Marco, Alexander D. Weston, Kenneth A. Philbrick, et al.. (2021). Generative Adversarial Networks to Synthesize Missing T1 and FLAIR MRI Sequences for Use in a Multisequence Brain Tumor Segmentation Model. Radiology. 299(2). 313–323. 76 indexed citations
14.
Blezek, Daniel J., William J. Ryan, Kenneth A. Philbrick, et al.. (2021). Deep Learning Improves the Temporal Reproducibility of Aortic Measurement. Journal of Digital Imaging. 34(5). 1183–1189. 7 indexed citations
15.
Cai, Jason, Zeynettin Akkus, Kenneth A. Philbrick, et al.. (2020). Fully Automated Segmentation of Head CT Neuroanatomy Using Deep Learning. Radiology Artificial Intelligence. 2(5). e190183–e190183. 29 indexed citations
16.
Cai, Jason, et al.. (2020). Asymptomatic presentation of a congenital malformation of the portal vein with portosystemic shunt. SHILAP Revista de lepidopterología. 15(10). 2009–2014.
17.
Erickson, Bradley J. & Jason Cai. (2020). Magician’s Corner: 4. Image Segmentation with U-Net. Radiology Artificial Intelligence. 2(1). e190161–e190161. 6 indexed citations
18.
Akkus, Zeynettin, Jason Cai, Arunnit Boonrod, et al.. (2019). A Survey of Deep-Learning Applications in Ultrasound: Artificial Intelligence–Powered Ultrasound for Improving Clinical Workflow. Journal of the American College of Radiology. 16(9). 1318–1328. 206 indexed citations
19.
Cai, Jason, et al.. (2018). Practical application of neural networks in assessing completion effectiveness in the Montney unconventional gas play in northeast British Columbia, Canada. Bulletin of Canadian Petroleum Geology. 66(2). 552–574. 1 indexed citations
20.
Meyer, Ted A., Rodney J. Schlosser, Zachary M. Soler, et al.. (2018). A Randomized Controlled Trial of Balloon Dilation as a Treatment for Persistent Eustachian Tube Dysfunction With 1-Year Follow-Up. Otology & Neurotology. 39(7). 894–902. 79 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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