32 citations · 48 across the 21 of their papers we have counts for
7 papers · 2 filters
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…
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…