Summary of A Two-stage Proactive Dialogue Generator For Efficient Clinical Information Collection Using Large Language Model, by Xueshen Li et al.
A Two-Stage Proactive Dialogue Generator for Efficient Clinical Information Collection Using Large Language Model
by Xueshen Li, Xinlong Hou, Nirupama Ravi, Ziyi Huang, Yu Gan
First submitted to arxiv on: 2 Oct 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 A novel approach to automating patient-doctor interactions for more efficient disease diagnosis is proposed. The authors develop a diagnostic dialogue system that uses medical history and conversation logic to generate multi-round clinical queries. This system addresses the challenges of under-exploration and non-flexibility in dialogue generation by incorporating a two-stage recommendation structure and carefully designed ranking criteria. The model is trained on a real-world medical conversation dataset and shown to generate clinical queries with efficient fluency, professionalism, and safety, while effectively collecting relevant disease diagnostic information. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Doctors want to know more about patients’ symptoms and medical history to make better diagnoses. To help them do this faster, the authors created a computer program that helps collect important information during conversations. The program uses logic to ask questions like a real doctor would, making sure it gets all the right information. It also makes sure the conversation is safe and professional. The authors tested their system with real medical data and found it worked well. |
