Text2MDT: Extracting Decision Trees from Medical Texts Using Large Language Models
Authors: Wei Zhu, Kechen Liu, Jiajun Tian, Yi Zhao, Zehao Li, Zequan Liu, Ming Tan


Reviewer paper summaries: The paper proposes a method to extract a medical decision tree from medical text.
It proposed and compared the performance between the pipeline and LLM-based end-to-end approach.
The authors generate a dataset for the experiment and evaluate the models using them.
Summary of strengths: Extracting a medical decision tree from text is challenging, and there are few well-labeled resources.
They generate the Chinese dataset for this task.
Summary of weaknesses: The text is not logically clear throughout the whole paper, and it is very difficult to follow what the authors are trying to explain.
Comments, suggestions and typos: The content of Task Formulation in sec 3.1, along with Figure 1, is difficult to follow. The authors are advised to review it thoroughly.
Overall assessment scores: 2