English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Tools annual

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Presents the access of premium content as premium feature

Premium Content

Global: Zendy Plus annual

Global: Zendy Free Plan

It is becoming increasingly emphasis on the importance of LLM participatingin clinical diagnosis decision-making. However, the low specialization refersto that current medical LLMs can not provide specific medical advice, which aremore like a medical Q\&A. And there is no suitable clinical guidance tree dataset that can be used directly with LLM. To address this issue, we first proposeLLM-executavle clinical guidance tree(CGT), which can be directly used by largelanguage models, and construct medical diagnostic decision-making dataset(MedDM), from flowcharts in clinical practice guidelines. We propose anapproach to screen flowcharts from medical literature, followed by theiridentification and conversion into standardized diagnostic decision trees.Constructed a knowledge base with 1202 decision trees, which came from 5000medical literature and covered 12 hospital departments, including internalmedicine, surgery, psychiatry, and over 500 diseases.Moreover, we propose amethod for reasoning on LLM-executable CGT and a Patient-LLM multi-turndialogue framework.

MedDM:LLM-executable clinical guidance tree for clinical decision-making