Summary of Rjua-meddqa: a Multimodal Benchmark For Medical Document Question Answering and Clinical Reasoning, by Congyun Jin et al.
RJUA-MedDQA: A Multimodal Benchmark for Medical Document Question Answering and Clinical Reasoning
by Congyun Jin, Ming Zhang, Xiaowei Ma, Li Yujiao, Yingbo Wang, Yabo Jia, Yuliang Du, Tao Sun, Haowen Wang, Cong Fan, Jinjie Gu, Chenfei Chi, Xiangguo Lv, Fangzhou Li, Wei Xue, Yiran Huang
First submitted to arxiv on: 19 Feb 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Applications (stat.AP)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary Recent advancements in Large Language Models (LLMs) and Large Multi-modal Models (LMMs) have shown potential in various medical applications, such as Intelligent Medical Diagnosis. Although impressive results have been achieved, we find that existing benchmarks do not reflect the complexity of real medical reports and specialized in-depth reasoning capabilities. To address this gap, we introduced RJUA-MedDQA, a comprehensive benchmark in the field of medical specialization, which poses several challenges: comprehensively interpreting image content across diverse challenging layouts, possessing numerical reasoning ability to identify abnormal indicators, and demonstrating clinical reasoning ability to provide statements of disease diagnosis, status, and advice based on medical contexts. We designed the data generation pipeline and proposed the Efficient Structural Restoration Annotation (ESRA) Method, aimed at restoring textual and tabular content in medical report images. This method substantially enhances annotation efficiency, doubling the productivity of each annotator, and yields a 26.8% improvement in accuracy. We conducted extensive evaluations, including few-shot assessments of 5 LMMs capable of solving Chinese medical QA tasks. To further investigate the limitations and potential of current LMMs, we conducted comparative experiments on a set of strong LLMs using image-text generated by ESRA method. Our results show that existing LMMs are still limited in their performance, but they exhibit greater robustness to low-quality and diverse-structured images compared to traditional LLMs. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about creating a new benchmark for testing how well large language models can understand medical reports. The goal is to help machines diagnose diseases better by giving them real-world challenges to overcome. To do this, we created a special dataset called RJUA-MedDQA that has many different types of images and text that need to be understood together. We also made a new way to annotate these datasets, which makes it easier and more accurate for humans to prepare the data. The paper shows that existing models are not very good at understanding medical reports yet, but they do get better when faced with low-quality or unusual images. The results of this research can help make machines better at diagnosing diseases and improve healthcare. |
Keywords
» Artificial intelligence » Few shot » Multi modal
