Summary of Medpromptx: Grounded Multimodal Prompting For Chest X-ray Diagnosis, by Mai A. Shaaban et al.
MedPromptX: Grounded Multimodal Prompting for Chest X-ray Diagnosis
by Mai A. Shaaban, Adnan Khan, Mohammad Yaqub
First submitted to arxiv on: 22 Mar 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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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 This paper introduces MedPromptX, a clinical decision support system that integrates multimodal large language models (MLLMs), few-shot prompting (FP), and visual grounding (VG) to combine chest X-ray images with structured clinical data for diagnosis. The system utilizes pre-trained MLLMs to complement incomplete electronic health records (EHR), providing a comprehensive understanding of patients’ medical history. Few-shot prompting reduces the need for extensive training of MLLMs, tackling hallucination issues. However, selecting optimal few-shot examples and high-quality candidates can be burdensome, influencing model performance. To address this, the authors propose a dynamic refinement technique for real-time adjustments to new patient scenarios. Visual grounding narrows the search area in X-ray images, enhancing abnormality identification. The paper also releases MedPromptX-VQA, an in-context visual question answering dataset derived from MIMIC-IV and MIMIC-CXR-JPG databases. Results show that MedPromptX achieves a 11% improvement in F1-score compared to baselines. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research paper is about creating a new tool called MedPromptX, which helps doctors diagnose diseases from chest X-ray images and medical records. The tool uses special computer models to fill in missing information on patients’ health histories. It also makes it easier for the models to learn by giving them only a few examples of correct diagnoses. The authors tested their tool on a big dataset of chest X-ray images and medical records, and found that it did a much better job than other tools. This is important because doctors need help diagnosing diseases quickly and accurately. The paper also includes a new dataset of questions about X-ray images that can be used to test the tool’s ability to answer questions. |
Keywords
» Artificial intelligence » F1 score » Few shot » Grounding » Hallucination » Prompting » Question answering
