Seong J. Jang

517 total citations · 1 hit paper
24 papers, 283 citations indexed

About

Seong J. Jang is a scholar working on Surgery, Biomedical Engineering and Health Informatics. According to data from OpenAlex, Seong J. Jang has authored 24 papers receiving a total of 283 indexed citations (citations by other indexed papers that have themselves been cited), including 22 papers in Surgery, 4 papers in Biomedical Engineering and 2 papers in Health Informatics. Recurrent topics in Seong J. Jang's work include Orthopaedic implants and arthroplasty (19 papers), Total Knee Arthroplasty Outcomes (18 papers) and Orthopedic Infections and Treatments (10 papers). Seong J. Jang is often cited by papers focused on Orthopaedic implants and arthroplasty (19 papers), Total Knee Arthroplasty Outcomes (18 papers) and Orthopedic Infections and Treatments (10 papers). Seong J. Jang collaborates with scholars based in United States, Greece and Switzerland. Seong J. Jang's co-authors include Jonathan M. Vigdorchik, Peter K. Sculco, David J. Mayman, Seth A. Jerabek, Kyle N. Kunze, Drake G. LeBrun, Theofilos Karasavvidis, John R. Steele, Christopher G. Anderson and Ayoosh Pareek and has published in prestigious journals such as Cognitive Science, Journal of Shoulder and Elbow Surgery and The Journal of Arthroplasty.

In The Last Decade

Seong J. Jang

21 papers receiving 277 citations

Hit Papers

align trajectories

What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

Geographic Variation in Knee Phenotypes Based on the Coronal Plane Alignment of the Knee Classification: A Systematic Review

2023 61 citations Theofilos Karasavvidis, Drake G. LeBrun et al. The Journal of Arthroplasty profile →

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Seong J. Jang United States	11	229	52	37	26	20	24	283
Pieter D’Hooghe Qatar	10	152 0.7×	41 0.8×	41 1.1×	29 1.1×	1 0.1×	20	283
Jae‐Sung An France	10	175 0.8×	41 0.8×	10 0.3×	44 1.7×		31	220
Wongthawat Liawrungrueang Thailand	9	133 0.6×	62 1.2×	7 0.2×	10 0.4×	2 0.1×	67	201
Michael A. Gaudiani United States	9	290 1.3×	21 0.4×	8 0.2×	19 0.7×	2 0.1×	36	306
Han Gyeol Choi South Korea	10	224 1.0×	87 1.7×	5 0.1×	58 2.2×	2 0.1×	21	275
Rodrigo Moreira Portugal	5	91 0.4×	52 1.0×	28 0.8×	12 0.5×	1 0.1×	6	267
Luca Andriollo Italy	10	221 1.0×	22 0.4×	12 0.3×	7 0.3×		49	232
Grégoire Micicoi France	12	369 1.6×	51 1.0×	3 0.1×	42 1.6×		41	384
Alexandra T. Bourdillon United States	7	47 0.2×	8 0.2×	4 0.1×	8 0.3×	4 0.2×	24	119
Karadi Hari Sunil Kumar United Kingdom	10	202 0.9×	23 0.4×	4 0.1×	28 1.1×		24	226

Countries citing papers authored by Seong J. Jang

Since Specialization

Citations

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

Fields of papers citing papers by Seong J. Jang

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Seong J. Jang. 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 Seong J. Jang. The network helps show where Seong J. Jang may publish in the future.

Co-authorship network of co-authors of Seong J. Jang

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

All Works

Sort: Min cites: Since: Top N: Style:

20 of 20 papers shown

Jang, Seong J., et al.. (2024). Artificial Intelligence for Clinically Meaningful Outcome Prediction in Orthopedic Research: Current Applications and Limitations. Current Reviews in Musculoskeletal Medicine. 17(6). 185–206. 6 indexed citations

Jang, Seong J., Kyle N. Kunze, David J. Mayman, et al.. (2024). Variability of the femoral mechanical-anatomical axis angle and its implications in primary and revision total knee arthroplasty. Bone & Joint Open. 5(2). 101–108. 2 indexed citations

Jang, Seong J., Daniel A. Driscoll, Christopher G. Anderson, et al.. (2024). Radiographic Findings Associated With Mild Hip Dysplasia in 3869 Patients Using a Deep Learning Measurement Tool. Arthroplasty Today. 28. 101398–101398.

Jang, Seong J., Mark Alan Fontana, Kyle N. Kunze, et al.. (2023). An Interpretable Machine Learning Model for Predicting 10-Year Total Hip Arthroplasty Risk. The Journal of Arthroplasty. 38(7). S44–S50.e6. 10 indexed citations

Jang, Seong J., Kyle Alpaugh, Kyle N. Kunze, et al.. (2023). Deep-Learning Automation of Preoperative Radiographic Parameters Associated With Early Periprosthetic Femur Fracture After Total Hip Arthroplasty. The Journal of Arthroplasty. 39(5). 1191–1198.e2. 1 indexed citations

Anderson, Christopher G., Seong J. Jang, David J. Mayman, et al.. (2023). Complication Rate After Primary Total Hip Arthroplasty Using the Posterior Approach and Enabling Technology: A Consecutive Series of 2,888 Hips. The Journal of Arthroplasty. 38(7). S119–S123.e3. 11 indexed citations

Steele, John R., Seong J. Jang, David J. Mayman, et al.. (2023). Deep Learning Phenotype Automation and Cohort Analyses of 1,946 Knees Using the Coronal Plane Alignment of the Knee Classification. The Journal of Arthroplasty. 38(6). S215–S221.e1. 37 indexed citations

Oeding, Jacob F., Riley J. Williams, Christopher L. Camp, et al.. (2023). A practical guide to the development and deployment of deep learning models for the orthopedic surgeon: part II. Knee Surgery Sports Traumatology Arthroscopy. 31(5). 1635–1643. 26 indexed citations

Jang, Seong J., et al.. (2023). The Impact of Varying Femoral Head Length on Hip External Rotation During Posterior-approach Total Hip Arthroplasty. Arthroplasty Today. 19. 101072–101072. 1 indexed citations

10.

Karasavvidis, Theofilos, et al.. (2023). Geographic Variation in Knee Phenotypes Based on the Coronal Plane Alignment of the Knee Classification: A Systematic Review. The Journal of Arthroplasty. 38(9). 1892–1899.e1. 61 indexed citations breakdown →

11.

Jang, Seong J., Kyle N. Kunze, Troy D. Bornes, et al.. (2023). Leg-Length Discrepancy Variability on Standard Anteroposterior Pelvis Radiographs: An Analysis Using Deep Learning Measurements. The Journal of Arthroplasty. 38(10). 2017–2023.e3. 7 indexed citations

12.

Jang, Seong J., Kyle N. Kunze, David J. Mayman, et al.. (2022). Comparison of tibial alignment parameters based on clinically relevant anatomical landmarks. Bone & Joint Open. 3(10). 767–776. 15 indexed citations

13.

Jang, Seong J., Kyle N. Kunze, Jonathan M. Vigdorchik, et al.. (2022). John Charnley Award: Deep Learning Prediction of Hip Joint Center on Standard Pelvis Radiographs. The Journal of Arthroplasty. 37(7). S400–S407.e1. 16 indexed citations

14.

Kunze, Kyle N., Seong J. Jang, Tim Li, et al.. (2022). Radiographic findings involved in knee osteoarthritis progression are associated with pain symptom frequency and baseline disease severity: a population‐level analysis using deep learning. Knee Surgery Sports Traumatology Arthroscopy. 31(2). 586–595. 9 indexed citations

15.

Malahias, Michael‐Alexander, Ioannis Gkiatas, Seong J. Jang, et al.. (2022). The modern Burch-Schneider antiprotrusio cage for the treatment of acetabular defects: is it still an option? A systematic review. Hip International. 33(4). 705–715. 8 indexed citations

16.

Jang, Seong J., et al.. (2022). Abnormal spinopelvic mobility as a risk factor for acetabular placement error in total hip arthroplasty using optical computer-assisted surgical navigation system. Bone & Joint Open. 3(6). 475–484. 10 indexed citations

17.

Malahias, Michael‐Alexander, Seong J. Jang, Alex Gu, et al.. (2021). Polyethylene liner cementation into a well-fixed metal acetabular shell for the management of periacetabular osteolysis: a systematic review. European Journal of Orthopaedic Surgery & Traumatology. 32(8). 1459–1468. 2 indexed citations

18.

Malahias, Michael‐Alexander, Fabio Mancino, Seong J. Jang, et al.. (2021). Safety and outcome of simultaneous bilateral unicompartmental knee arthroplasty: A systematic review. Journal of Orthopaedics. 24. 58–64. 10 indexed citations

19.

Malahias, Michael‐Alexander, Seong J. Jang, Alex Gu, et al.. (2020). Cervical spine degenerative disease is an independent risk factor for increased revision rate following total knee arthroplasty. European Journal of Orthopaedic Surgery & Traumatology. 31(3). 511–516. 2 indexed citations

20.

Musslick, Sebastian, Seong J. Jang, Michael Shvartsman, Amitai Shenhav, & Jonathan D. Cohen. (2018). Constraints associated with cognitive control and the stability-flexibility dilemma.. Cognitive Science. 21 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.

Explore authors with similar magnitude of impact