Summary of Medical Dialogue: a Survey Of Categories, Methods, Evaluation and Challenges, by Xiaoming Shi et al.
Medical Dialogue: A Survey of Categories, Methods, Evaluation and Challenges
by Xiaoming Shi, Zeming Liu, Li Du, Yuxuan Wang, Hongru Wang, Yuhang Guo, Tong Ruan, Jie Xu, Shaoting Zhang
First submitted to arxiv on: 17 May 2024
Categories
- Main: Computation and Language (cs.CL)
- 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 provides a comprehensive review and organization of research on medical dialog systems, which is crucial for advancing this field. The authors investigate 325 papers from computer science and natural language processing conferences and journals to identify categories, methods, and evaluation metrics used in medical dialogue systems. Recent advances in large language models have reshaped the foundation of medical dialog systems, but despite their potential, current systems still face challenges. The paper highlights the grand challenges facing medical dialog systems, particularly those related to large language models. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at how computers can talk with doctors and patients. Right now, there isn’t a clear way to understand all the research that has been done on this topic. So, the authors looked at 325 papers from important conferences and journals to see what categories, methods, and ways of measuring success are used in medical dialogue systems. They found that recent advancements in big language models have changed how we approach this field, but there are still many problems to solve. |
Keywords
» Artificial intelligence » Natural language processing
