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20242026
most citedEnhancing Clinical Efficiency through LLM: Discharge Note Generation for Cardiac Patients

7 citations · 7 across the 4 of their papers we have counts for

collaborators

5 papers

cs.IR2026

Ruling Out to Rule In: Contrastive Hypothesis Retrieval for Medical Question Answering

Byeolhee Kim, Min-Kyung Kim, Young-Hak Kim +1

Retrieval-augmented generation (RAG) grounds large language models in external medical knowledge, yet standard retrievers frequently surface hard negatives that are semantically cl…

cs.CL2025

Quality-Aware Translation Tagging in Multilingual RAG system

Hoyeon Moon, Byeolhee Kim, Nikhil Verma

Multilingual Retrieval-Augmented Generation (mRAG) often retrieves English documents and translates them into the query language for low-resource settings. However, poor translatio…

cs.CV2024

Mitigating Adversarial Attacks in LLMs through Defensive Suffix Generation

Minkyoung Kim, Yunha Kim, Hyeram Seo +9

Large language models (LLMs) have exhibited outstanding performance in natural language processing tasks. However, these models remain susceptible to adversarial attacks in which s…

cs.CL20247 cited

Enhancing Clinical Efficiency through LLM: Discharge Note Generation for Cardiac Patients

HyoJe Jung, Yunha Kim, Heejung Choi +10

Medical documentation, including discharge notes, is crucial for ensuring patient care quality, continuity, and effective medical communication. However, the manual creation of the…

cs.CV2024

InMD-X: Large Language Models for Internal Medicine Doctors

Hansle Gwon, Imjin Ahn, Hyoje Jung +3

In this paper, we introduce InMD-X, a collection of multiple large language models specifically designed to cater to the unique characteristics and demands of Internal Medicine Doc…