32 citations · 48 across the 21 of their papers we have counts for
21 papers
BioMedArena: An Open-source Toolkit for Building and Evaluating Biomedical Deep Research Agents
Jinge Wu, Hongjian Zhou, Mingde Zeng +8
Reproducing and comparing deep research agents today is hard: the same backbone evaluated on the same benchmark can report different accuracies across papers because the harness an…
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…
Graph-based LLM over Semi-Structured Population Data for Dynamic Policy Response
Daqian Shi, Xiaolei Diao, Jinge Wu +4
Timely and accurate analysis of population-level data is crucial for effective decision-making during public health emergencies such as the COVID-19 pandemic. However, the massive…
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…
Adverse Event Extraction from Discharge Summaries: A New Dataset, Annotation Scheme, and Initial Findings
Imane Guellil, Salomé Andres, Atul Anand +5
In this work, we present a manually annotated corpus for Adverse Event (AE) extraction from discharge summaries of elderly patients, a population often underrepresented in clinical…
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…
Towards deployment-centric multimodal AI beyond vision and language
Xianyuan Liu, Jiayang Zhang, Shuo Zhou +45
Multimodal artificial intelligence (AI) integrates diverse types of data via machine learning to improve understanding, prediction, and decision-making across disciplines such as h…
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…
RiskAgent: Synergizing Language Models with Validated Tools for Evidence-Based Risk Prediction
Fenglin Liu, Jinge Wu, Hongjian Zhou +9
Large Language Models (LLMs) achieve competitive results compared to human experts in medical examinations. However, it remains a challenge to apply LLMs to complex clinical decisi…
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…
Infusing clinical knowledge into tokenisers for language models
Abul Hasan, Jinge Wu, Quang Ngoc Nguyen +7
This study introduces a novel knowledge enhanced tokenisation mechanism, K-Tokeniser, for clinical text processing. Technically, at initialisation stage, K-Tokeniser populates glob…
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…
RadBARTsum: Domain Specific Adaption of Denoising Sequence-to-Sequence Models for Abstractive Radiology Report Summarization
Jinge Wu, Abul Hasan, Honghan Wu
Radiology report summarization is a crucial task that can help doctors quickly identify clinically significant findings without the need to review detailed sections of reports. Thi…
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…
A Hybrid Framework with Large Language Models for Rare Disease Phenotyping
Jinge Wu, Hang Dong, Zexi Li +7
Rare diseases pose significant challenges in diagnosis and treatment due to their low prevalence and heterogeneous clinical presentations. Unstructured clinical notes contain valua…
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…