7 citations · 11 across the 17 of their papers we have counts for
17 papers
Learning to Fuse LLMs with Ontology Rankers for Rare-Disease Diagnosis
Zhaoyang Jiang, Zhizhong Fu, Yunsoo Kim +5
Ontology rankers remain useful for rare-disease diagnosis because each candidate can be traced to matched patient phenotypes. Large language models (LLMs) can generate differential…
One Score, Two Decisions: Selective Prediction on the Rare-Disease Tail
Zhaoyang Jiang, Zhizhong Fu, Yunsoo Kim +5
Given a patient's clinical findings, a diagnostic system ranks possible diseases and must decide when to endorse its first prediction or defer it for review. This decision is usual…
Presentation, Not Mechanism: A Render Confound in Deprecation-Aware Memory Evaluation
Zhaoyang Jiang, Zhizhong Fu, Zicheng Li +5
AI systems increasingly retrieve from records that revise themselves: issue threads, encyclopedic histories, policy logs, and long conversations. The challenge is not only finding…
Better Accuracies, Worse Reasoning: A Step-Level Audit of Medical Chain-of-Thought Distillation
Zhaoyang Jiang, Xuanqi Peng, Fei Teng +5
Chain-of-thought (CoT) distillation trains a smaller model to imitate a teacher's reasoning trace, but it is typically evaluated by final-answer metrics including accuracy. We ask…
A Regime Theory of Controller Class Selection for LLM Action Decisions
Zhaoyang Jiang, Zhizhong Fu, Yunsoo Kim +4
Deployed language and vision-language models must decide, on each input, whether to answer directly, retrieve evidence, defer to a stronger model, or abstain. Contrary to the commo…
HARE: an entity and relation centric evaluation framework for histopathology reports
Yunsoo Kim, Michal W. S. Ong, Alex Shavick +2
Medical domain automated text generation is an active area of research and development; however, evaluating the clinical quality of generated reports remains a challenge, especiall…
RadEyeVideo: Enhancing general-domain Large Vision Language Model for chest X-ray analysis with video representations of eye gaze
Yunsoo Kim, Jinge Wu, Honghan Wu
Large Vision-Language Models (LVLMs) have demonstrated promising performance in chest X-ray (CXR) analysis. To enhance human-computer interaction, several studies have incorporated…
BioHopR: A Benchmark for Multi-Hop, Multi-Answer Reasoning in Biomedical Domain
Yunsoo Kim, Yusuf Abdulle, Honghan Wu
Biomedical reasoning often requires traversing interconnected relationships across entities such as drugs, diseases, and proteins. Despite the increasing prominence of large langua…
Look & Mark: Leveraging Radiologist Eye Fixations and Bounding boxes in Multimodal Large Language Models for Chest X-ray Report Generation
Yunsoo Kim, Jinge Wu, Su-Hwan Kim +3
Recent advancements in multimodal Large Language Models (LLMs) have significantly enhanced the automation of medical image analysis, particularly in generating radiology reports fr…
IHC-LLMiner: Automated extraction of tumour immunohistochemical profiles from PubMed abstracts using large language models
Yunsoo Kim, Michal W. S. Ong, Daniel W. Rogalsky +3
Immunohistochemistry (IHC) is essential in diagnostic pathology and biomedical research, offering critical insights into protein expression and tumour biology. This study presents…
SLaVA-CXR: Small Language and Vision Assistant for Chest X-ray Report Automation
Jinge Wu, Yunsoo Kim, Daqian Shi +3
Inspired by the success of large language models (LLMs), there is growing research interest in developing LLMs in the medical domain to assist clinicians. However, for hospitals, u…
MedExQA: Medical Question Answering Benchmark with Multiple Explanations
Yunsoo Kim, Jinge Wu, Yusuf Abdulle +1
This paper introduces MedExQA, a novel benchmark in medical question-answering, to evaluate large language models' (LLMs) understanding of medical knowledge through explanations. B…
Chain-of-Though (CoT) prompting strategies for medical error detection and correction
Zhaolong Wu, Abul Hasan, Jinge Wu +4
This paper describes our submission to the MEDIQA-CORR 2024 shared task for automatically detecting and correcting medical errors in clinical notes. We report results for three met…
Error Correction in Radiology Reports: A Knowledge Distillation-Based Multi-Stage Framework
Jinge Wu, Zhaolong Wu, Ruizhe Li +6
The increasing complexity and workload of clinical radiology leads to inevitable oversights and mistakes in their use as diagnostic tools, causing delayed treatments and sometimes…
Enhancing Human-Computer Interaction in Chest X-ray Analysis using Vision and Language Model with Eye Gaze Patterns
Yunsoo Kim, Jinge Wu, Yusuf Abdulle +2
Recent advancements in Computer Assisted Diagnosis have shown promising performance in medical imaging tasks, particularly in chest X-ray analysis. However, the interaction between…
Hallucination Benchmark in Medical Visual Question Answering
Jinge Wu, Yunsoo Kim, Honghan Wu
The recent success of large language and vision models (LLVMs) on vision question answering (VQA), particularly their applications in medicine (Med-VQA), has shown a great potentia…
Exploring Multimodal Large Language Models for Radiology Report Error-checking
Jinge Wu, Yunsoo Kim, Eva C. Keller +7
This paper proposes one of the first clinical applications of multimodal large language models (LLMs) as an assistant for radiologists to check errors in their reports. We created…