5 papers
Mixture of Multicenter Experts in Multimodal AI for Debiased Radiotherapy Target Delineation
Yujin Oh, Sangjoon Park, Xiang Li +20
Clinical decision-making reflects diverse strategies shaped by regional patient populations and institutional protocols. However, most existing medical artificial intelligence (AI)…
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…
End-to-End Breast Cancer Radiotherapy Planning via LMMs with Consistency Embedding
Kwanyoung Kim, Yujin Oh, Sangjoon Park +5
Recent advances in AI foundation models have significant potential for lightening the clinical workload by mimicking the comprehensive and multi-faceted approaches used by medical…
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…
LLM-driven Multimodal Target Volume Contouring in Radiation Oncology
Yujin Oh, Sangjoon Park, Hwa Kyung Byun +4
Target volume contouring for radiation therapy is considered significantly more challenging than the normal organ segmentation tasks as it necessitates the utilization of both imag…