2 citations · 2 across the 6 of their papers we have counts for
6 papers
DuetMoE: Coupling Inter- and Intra-Subgroup Robustness for Fair Medical Image Analysis
Yiqi Tian, Sangjoon Park, Jinwoong Park +4
As medical AI expands across diverse healthcare settings worldwide, equitable performance across patient populations is becoming essential to trustworthy clinical use. Fairness in…
CoMMa: Contribution-Aware Medical Multi-Agents for Decentralized Oncology Decision Support
Yichen Wu, Kailong Fan, Sangjoon Park +9
Recent multi-agent frameworks have shown promise for oncology decision support, yet most assume centralized data access and rely on prompt-based assignment, limiting their applicab…
Susceptibility of Large Language Models to User-Driven Factors in Medical Queries
Kyung Ho Lim, Ujin Kang, Xiang Li +4
Large language models (LLMs) are increasingly used in healthcare, but their reliability is heavily influenced by user-driven factors such as question phrasing and the completeness…
Distribution-aware Fairness Learning in Medical Image Segmentation From A Control-Theoretic Perspective
Yujin Oh, Pengfei Jin, Sangjoon Park +6
Ensuring fairness in medical image segmentation is critical due to biases in imbalanced clinical data acquisition caused by demographic attributes (e.g., age, sex, race) and clinic…
Mixture of Multicenter Experts in Multimodal AI for Debiased Radiotherapy Target Delineation
Yujin Oh, Sangjoon Park, Xiang Li +21
Clinical decision-making reflects diverse strategies shaped by regional patient populations and institutional protocols. However, most existing medical artificial intelligence (AI)…
Improving Cone-Beam CT Image Quality with Knowledge Distillation-Enhanced Diffusion Model in Imbalanced Data Settings
Joonil Hwang, Sangjoon Park, NaHyeon Park +2
In radiation therapy (RT), the reliance on pre-treatment computed tomography (CT) images encounter challenges due to anatomical changes, necessitating adaptive planning. Daily cone…