George Shih

4.2k total citations
70 papers, 1.6k citations indexed

About

George Shih is a scholar working on Radiology, Nuclear Medicine and Imaging, Pulmonary and Respiratory Medicine and Artificial Intelligence. According to data from OpenAlex, George Shih has authored 70 papers receiving a total of 1.6k indexed citations (citations by other indexed papers that have themselves been cited), including 46 papers in Radiology, Nuclear Medicine and Imaging, 23 papers in Pulmonary and Respiratory Medicine and 13 papers in Artificial Intelligence. Recurrent topics in George Shih's work include Radiomics and Machine Learning in Medical Imaging (20 papers), COVID-19 diagnosis using AI (14 papers) and Artificial Intelligence in Healthcare and Education (12 papers). George Shih is often cited by papers focused on Radiomics and Machine Learning in Medical Imaging (20 papers), COVID-19 diagnosis using AI (14 papers) and Artificial Intelligence in Healthcare and Education (12 papers). George Shih collaborates with scholars based in United States, China and Canada. George Shih's co-authors include Martin R. Prince, Yan Cao, Luciano M. Prevedello, Safwan S. Halabi, Marc Kohli, Adam E. Flanders, Katherine P. Andriole, Bradley J. Erickson, Jayashree Kalpathy–Cramer and Carol C. Wu and has published in prestigious journals such as Nature Communications, Radiology and The Journal of Urology.

In The Last Decade

George Shih

65 papers receiving 1.6k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
George Shih United States 19 1000 347 337 271 204 70 1.6k
Tarik K. Alkasab United States 20 754 0.8× 360 1.0× 143 0.4× 61 0.2× 267 1.3× 51 1.3k
Katy Blumer United States 6 838 0.8× 215 0.6× 328 1.0× 96 0.4× 171 0.8× 8 1.5k
Nicholas Stence United States 21 343 0.3× 94 0.3× 56 0.2× 79 0.3× 108 0.5× 78 1.5k
Tobias Penzkofer Germany 28 912 0.9× 56 0.2× 146 0.4× 27 0.1× 462 2.3× 124 2.0k
Daiju Ueda Japan 26 838 0.8× 620 1.8× 378 1.1× 68 0.3× 287 1.4× 123 2.3k
J. Titano United States 14 746 0.7× 394 1.1× 518 1.5× 13 0.0× 176 0.9× 31 1.7k
Keno K. Bressem Germany 26 785 0.8× 534 1.5× 427 1.3× 9 0.0× 229 1.1× 109 1.8k
Arkadiusz Miernik Germany 25 410 0.4× 80 0.2× 95 0.3× 14 0.1× 378 1.9× 185 2.4k
Daniel Tse United States 6 991 1.0× 268 0.8× 476 1.4× 13 0.0× 196 1.0× 7 1.5k
Paras Lakhani United States 18 1.3k 1.3× 343 1.0× 517 1.5× 8 0.0× 255 1.3× 39 2.0k

Countries citing papers authored by George Shih

Since Specialization
Citations

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

Fields of papers citing papers by George Shih

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of George Shih

This figure shows the co-authorship network connecting the top 25 collaborators of George Shih. A scholar is included among the top collaborators of George Shih 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 George Shih. George Shih 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.
Chevalier, James M., Akshay Goel, George Shih, et al.. (2024). Deep learning-based liver cyst segmentation in MRI for autosomal dominant polycystic kidney disease. PubMed. 1(2). umae014–umae014. 3 indexed citations
2.
Wada, Akihiko, Toshiaki Akashi, George Shih, et al.. (2024). Optimizing GPT-4 Turbo Diagnostic Accuracy in Neuroradiology through Prompt Engineering and Confidence Thresholds. Diagnostics. 14(14). 1541–1541. 13 indexed citations
3.
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
4.
Ball, Robyn L., Luciano M. Prevedello, Ferco H. Berger, et al.. (2024). RSNA 2023 Abdominal Trauma AI Challenge: Review and Outcomes. Radiology Artificial Intelligence. 7(1). e240334–e240334. 1 indexed citations
5.
Goel, Akshay, et al.. (2023). Effect of Averaging Measurements From Multiple MRI Pulse Sequences on Kidney Volume Reproducibility in Autosomal Dominant Polycystic Kidney Disease. Journal of Magnetic Resonance Imaging. 58(4). 1153–1160. 5 indexed citations
6.
Lin, Mingquan, Bojian Hou, Swati Mishra, et al.. (2023). Enhancing thoracic disease detection using chest X-rays from PubMed Central Open Access. Computers in Biology and Medicine. 159. 106962–106962. 5 indexed citations
7.
Shih, George, James M. Chevalier, Jon D. Blumenfeld, et al.. (2023). Test Retest Reproducibility of Organ Volume Measurements in ADPKD Using 3D Multimodality Deep Learning. Academic Radiology. 31(3). 889–899. 5 indexed citations
8.
Goel, Akshay, George Shih, Sunil Jeph, et al.. (2022). Deployed Deep Learning Kidney Segmentation for Polycystic Kidney Disease MRI. Radiology Artificial Intelligence. 4(2). e210205–e210205. 36 indexed citations
9.
Seah, Jarrel, Jay Gajera, Helen Kavnoudias, et al.. (2021). CLiP, catheter and line position dataset. Scientific Data. 8(1). 285–285. 10 indexed citations
10.
Shih, George, Carol C. Wu, Safwan S. Halabi, et al.. (2019). Augmenting the National Institutes of Health Chest Radiograph Dataset with Expert Annotations of Possible Pneumonia. Radiology Artificial Intelligence. 1(1). e180041–e180041. 181 indexed citations
11.
Prevedello, Luciano M., Safwan S. Halabi, George Shih, et al.. (2019). Challenges Related to Artificial Intelligence Research in Medical Imaging and the Importance of Image Analysis Competitions. Radiology Artificial Intelligence. 1(1). e180031–e180031. 107 indexed citations
12.
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
13.
Gao, Jing, et al.. (2018). Shear Wave Elastography to Assess False Vocal Folds in Healthy Adults: A Feasibility Study. Journal of Ultrasound in Medicine. 37(11). 2537–2544. 5 indexed citations
14.
Roy, Sharmili, Michael S. Brown, & George Shih. (2013). Visual Interpretation with Three-Dimensional Annotations (VITA): Three-Dimensional Image Interpretation Tool for Radiological Reporting. Journal of Digital Imaging. 27(1). 49–57. 4 indexed citations
15.
Gao, Jing, et al.. (2010). Pitfalls and Sources of Error of Color Duplex Sonography in Screening for Renovascular Hypertension. Nephro-Urology Monthly. 2(1). 212–223.
16.
Zhang, Yang, Xiao Ming Zhang, Joan C. Prowda, et al.. (2009). Changes in hepatic venous morphology with cirrhosis on MRI. Journal of Magnetic Resonance Imaging. 29(5). 1085–1092. 28 indexed citations
17.
Gao, Jing, et al.. (2007). Intrarenal Color Duplex Ultrasonography. Journal of Ultrasound in Medicine. 26(10). 1403–1418. 31 indexed citations
18.
19.
Shih, George. (1998). Illustrated Textbook of Pediatrics. The Yale Journal of Biology and Medicine. 71(1). 35–35.
20.
Shih, George. (1998). Bodies of Evidence: Reconstructing History through Skeletal Analysis. The Yale Journal of Biology and Medicine. 71(1). 43–43. 3 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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