Papers
17
Total Citations
593
H-Index
12
About
Hyungju Kwon is a pioneering surgical researcher whose work sits at the dynamic intersection of robotic surgery, endocrine surgery, and artificial intelligence-assisted surgical training. Based on a prolific body of work, Kwon has established himself as a leading authority on minimally invasive and robotic approaches to thyroid and adrenal surgery, with particular expertise in the bilateral axillo-breast approach (BABA) robotic thyroidectomy technique. His most impactful contribution—garnering 102 citations—applies deep learning to automatically evaluate surgical skills during robotic procedures, a breakthrough that addresses one of the field's most pressing training challenges. Alongside this, his work on intraoperative parathyroid localization using indocyanine green technology (92 citations) has meaningfully improved patient safety during complex neck surgery. His systematic review of adrenalectomy approaches (78 citations) has helped standardize surgical decision-making globally. Kwon has consistently pushed boundaries in robotic thyroidectomy, examining its safety for larger thyroid carcinomas, its application in Graves' disease, and its postoperative pain profile compared to open surgery. His quantitative learning curve analysis further reflects his commitment to surgical education and measurable competency. Collectively, his research has shaped how surgeons train, operate, and evaluate outcomes in the robotic era.
Research Focus
Key Achievements
Top Papers
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- 8Quantitative Assessment of the Learning Curve for Robotic Thyroid Surgery30 citations · 2019
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