Irene H. Suh

417 total citations
20 papers, 274 citations indexed

About

Irene H. Suh is a scholar working on Surgery, Biomedical Engineering and Computer Vision and Pattern Recognition. According to data from OpenAlex, Irene H. Suh has authored 20 papers receiving a total of 274 indexed citations (citations by other indexed papers that have themselves been cited), including 20 papers in Surgery, 13 papers in Biomedical Engineering and 7 papers in Computer Vision and Pattern Recognition. Recurrent topics in Irene H. Suh's work include Surgical Simulation and Training (20 papers), Anatomy and Medical Technology (12 papers) and Augmented Reality Applications (7 papers). Irene H. Suh is often cited by papers focused on Surgical Simulation and Training (20 papers), Anatomy and Medical Technology (12 papers) and Augmented Reality Applications (7 papers). Irene H. Suh collaborates with scholars based in United States, Spain and South Korea. Irene H. Suh's co-authors include Ka‐Chun Siu, Dmitry Oleynikov, Mukul Mukherjee, Nicholas Stergiou, Jung Hung Chien, Chad A. LaGrange, Bhavin C. Shah, Timothy N. Judkins, Srikant Vallabhajosula and Carl A. Nelson and has published in prestigious journals such as SHILAP Revista de lepidopterología, The Journal of Urology and Surgery.

In The Last Decade

Irene H. Suh

18 papers receiving 261 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Irene H. Suh United States 9 166 89 61 48 44 20 274
David Defriend United Kingdom 4 218 1.3× 93 1.0× 74 1.2× 38 0.8× 39 0.9× 6 361
Jackie Cha United States 10 170 1.0× 92 1.0× 25 0.4× 83 1.7× 73 1.7× 39 397
Amine Chellali France 12 206 1.2× 97 1.1× 112 1.8× 67 1.4× 39 0.9× 33 403
Patrick Wucherer Germany 8 206 1.2× 109 1.2× 76 1.2× 34 0.7× 13 0.3× 14 293
Philipp Stefan Germany 9 261 1.6× 143 1.6× 97 1.6× 38 0.8× 14 0.3× 19 365
Tony Tien United Kingdom 6 178 1.1× 31 0.3× 37 0.6× 15 0.3× 40 0.9× 8 317
Daniel Laxar Austria 6 99 0.6× 60 0.7× 67 1.1× 22 0.5× 17 0.4× 20 317
Christopher N. Andrews United States 7 81 0.5× 63 0.7× 86 1.4× 82 1.7× 10 0.2× 12 329
Sebastian Zeiner Austria 9 150 0.9× 118 1.3× 67 1.1× 24 0.5× 17 0.4× 14 445
Marc Lazarovici Germany 12 263 1.6× 129 1.4× 134 2.2× 46 1.0× 23 0.5× 46 516

Countries citing papers authored by Irene H. Suh

Since Specialization
Citations

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

Fields of papers citing papers by Irene H. Suh

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Irene H. Suh. 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 Irene H. Suh. The network helps show where Irene H. Suh may publish in the future.

Co-authorship network of co-authors of Irene H. Suh

This figure shows the co-authorship network connecting the top 25 collaborators of Irene H. Suh. A scholar is included among the top collaborators of Irene H. Suh 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 Irene H. Suh. Irene H. Suh 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.
Li, Yucheng, Victoria A. Nelson, C.T. Nguyen, et al.. (2025). Development of New Generation Portable Camera-Aided Surgical Simulator for Cognitive Training in Laparoscopic Cholecystectomy. Electronics. 14(4). 793–793.
2.
Nelson, Victoria A., et al.. (2024). Improved Portable Instrument Tracking System for Surgical Training. PubMed. 2024. 1 indexed citations
3.
Suh, Irene H., et al.. (2023). Current Perspective of Metaverse Application in Medical Education, Research and Patient Care. SHILAP Revista de lepidopterología. 2(2). 115–128. 44 indexed citations
4.
Suh, Irene H., Chad A. LaGrange, Dmitry Oleynikov, & Ka‐Chun Siu. (2015). Evaluating Robotic Surgical Skills Performance Under Distractive Environment Using Objective and Subjective Measures. Surgical Innovation. 23(1). 78–89. 9 indexed citations
5.
Vallabhajosula, Srikant, Timothy N. Judkins, Mukul Mukherjee, et al.. (2013). Skills Learning in Robot-Assisted Surgery Is Benefited by Task-Specific Augmented Feedback. Surgical Innovation. 20(6). 639–647. 3 indexed citations
6.
Suh, Irene H., et al.. (2013). Examination of Muscle Effort and Fatigue During Virtual and Actual Laparoscopic Surgical Skills Practice. Studies in health technology and informatics. 184. 122–8. 4 indexed citations
7.
Suh, Irene H., et al.. (2012). Investigating the Muscle Activities of Performing Surgical Training Tasks Using a Virtual Simulator. Studies in health technology and informatics. 173. 200–4. 2 indexed citations
8.
Chien, Jung Hung, et al.. (2012). Enhancing Fundamental Robot-Assisted Surgical Proficiency by Using a Portable Virtual Simulator. Surgical Innovation. 20(2). 198–203. 14 indexed citations
9.
10.
Suh, Irene H., Mukul Mukherjee, Dmitry Oleynikov, & Ka‐Chun Siu. (2011). Training program for fundamental surgical skill in robotic laparoscopic surgery. International Journal of Medical Robotics and Computer Assisted Surgery. 7(3). 327–333. 31 indexed citations
11.
Suh, Irene H., Mukul Mukherjee, Bhavin C. Shah, Dmitry Oleynikov, & Ka‐Chun Siu. (2011). Retention of fundamental surgical skills learned in robot-assisted surgery. Journal of Robotic Surgery. 6(4). 301–309. 7 indexed citations
12.
Suh, Irene H., et al.. (2011). Modeling surgical skill learning with cognitive simulation.. PubMed. 163. 428–32. 3 indexed citations
13.
Suh, Irene H., et al.. (2011). Electromyographic Correlates of Learning during Robotic Surgical Training in Virtual Reality. Studies in health technology and informatics. 163. 630–4. 4 indexed citations
14.
Chien, Jung Hung, et al.. (2010). Accuracy and speed trade‐off in robot‐assisted surgery. International Journal of Medical Robotics and Computer Assisted Surgery. 6(3). 324–329. 21 indexed citations
15.
Chien, Jung Hung, et al.. (2010). Innovative effector design for simulation training in robotic surgery. 2010 3rd International Conference on Biomedical Engineering and Informatics. 1457. 1755–1759. 5 indexed citations
16.
Suh, Irene H., et al.. (2010). The negative effect of distraction on performance of robot‐assisted surgical skills in medical students and residents. International Journal of Medical Robotics and Computer Assisted Surgery. 6(4). 377–381. 24 indexed citations
17.
Siu, Ka‐Chun, Irene H. Suh, Mukul Mukherjee, Dmitry Oleynikov, & Nicholas Stergiou. (2010). The Effect of Music on Robot-Assisted Laparoscopic Surgical Performance. Surgical Innovation. 17(4). 306–311. 47 indexed citations
18.
Siu, Ka‐Chun, Irene H. Suh, Mukul Mukherjee, Dmitry Oleynikov, & Nicholas Stergiou. (2009). The impact of environmental noise on robot-assisted laparoscopic surgical performance. Surgery. 147(1). 107–113. 44 indexed citations
19.
Mukherjee, Mukul, et al.. (2009). A Virtual Reality Training Program for Improvement of Robotic Surgical Skills. Studies in health technology and informatics. 142. 210–4. 8 indexed citations
20.
Suh, Irene H., et al.. (2009). Consistency of Performance of Robot-Assisted Surgical Tasks in Virtual Reality. Studies in health technology and informatics. 142. 369–73. 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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