MedDM:LLM-executable clinical guidance tree for clinical decision-making
This addresses the problem of low specialization in medical LLMs for clinicians, though it is incremental as it builds on existing guidance trees.
The authors tackled the lack of specialized medical advice from LLMs in clinical diagnosis by creating MedDM, a dataset of 1202 decision trees from 5000 medical literature across 12 departments and over 500 diseases, enabling LLM-executable clinical guidance trees.
It is becoming increasingly emphasis on the importance of LLM participating in clinical diagnosis decision-making. However, the low specialization refers to that current medical LLMs can not provide specific medical advice, which are more like a medical Q\&A. And there is no suitable clinical guidance tree data set that can be used directly with LLM. To address this issue, we first propose LLM-executavle clinical guidance tree(CGT), which can be directly used by large language models, and construct medical diagnostic decision-making dataset (MedDM), from flowcharts in clinical practice guidelines. We propose an approach to screen flowcharts from medical literature, followed by their identification and conversion into standardized diagnostic decision trees. Constructed a knowledge base with 1202 decision trees, which came from 5000 medical literature and covered 12 hospital departments, including internal medicine, surgery, psychiatry, and over 500 diseases.Moreover, we propose a method for reasoning on LLM-executable CGT and a Patient-LLM multi-turn dialogue framework.