Summary of Evaluating the Application Of Chatgpt in Outpatient Triage Guidance: a Comparative Study, by Dou Liu et al.
Evaluating the Application of ChatGPT in Outpatient Triage Guidance: A Comparative Study
by Dou Liu, Ying Han, Xiandi Wang, Xiaomei Tan, Di Liu, Guangwu Qian, Kang Li, Dan Pu, Rong Yin
First submitted to arxiv on: 27 Apr 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Human-Computer Interaction (cs.HC)
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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 The integration of Artificial Intelligence (AI) in healthcare has transformative potential for enhancing operational efficiency and health outcomes. Large Language Models (LLMs), such as ChatGPT, have shown capabilities in supporting medical decision-making. This study evaluates the consistency of responses provided by ChatGPT in outpatient guidance, including within-version response analysis and between-version comparisons. The results indicate that internal response consistency for ChatGPT-4.0 is significantly higher than ChatGPT-3.5, but between-version consistency is relatively low. Additionally, only 50% top recommendations match perfectly between the two versions. The findings offer insights into AI-assisted outpatient operations and facilitate exploration of LLMs in healthcare utilization. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary ChatGPT helps doctors make better decisions by giving them information about patients. This study looked at how well ChatGPT does this job, especially when it comes to triaging patients who need medical help quickly. The results show that one version of ChatGPT is more consistent than another, but they don’t agree very much. This means we can learn more about using AI in healthcare and make it better. |
