Summary of Automatic Medical Report Generation: Methods and Applications, by Li Guo et al.
Automatic Medical Report Generation: Methods and Applications
by Li Guo, Anas M. Tahir, Dong Zhang, Z. Jane Wang, Rabab K. Ward
First submitted to arxiv on: 26 Aug 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 A novel AI-based approach is proposed to address the growing demand for medical imaging diagnostics, where human radiologists are overwhelmed. The study reviews automatic medical report generation (AMRG) methods from 2021 to 2024, discussing primary challenges, applications across various imaging modalities, publicly available datasets, evaluation metrics, and techniques that enhance model performance. The paper aims to provide a comprehensive understanding of the existing literature and stimulate future research. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper looks at how AI can help with medical imaging reports. Right now, there aren’t enough radiologists to keep up with the number of images being taken. This is causing delays and possibly even mistakes in diagnosis. The study explores ways that AI can be used to generate these reports automatically. It covers what are called automatic medical report generation (AMRG) methods from 2021 to 2024. |
