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Related Experiment Video

Updated: Jan 8, 2026

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

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Published on: December 6, 2024

Streamlining evidence based clinical recommendations with large language models.

Dubai Li¹, Nan Jiang², Kangping Huang²

¹College of Biomedical Engineering and Instrument Science, Zhejiang University, Engineering Research Center of EMR and Intelligent Expert System, Ministry of Education, Hangzhou, China.

NPJ Digital Medicine

|December 21, 2025

Summary

This summary is machine-generated.

This study introduces Quicker, an AI system that speeds up clinical evidence synthesis and recommendation generation. Quicker helps clinicians make faster, more reliable evidence-based healthcare decisions.

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Area of Science:

Artificial Intelligence in Healthcare
Clinical Decision Support Systems
Evidence-Based Medicine

Background:

Integrating clinical evidence into practice is difficult due to workload and time constraints.
Current methods for evidence synthesis are time-consuming and resource-intensive.

Purpose of the Study:

To develop and evaluate Quicker, a Large Language Model (LLM)-powered system for automating clinical evidence synthesis and recommendation generation.
To assess Quicker's ability to replicate standard guideline development workflows.

Main Methods:

Developed Quicker, an end-to-end pipeline from clinical questions to recommendations.
Created Q2CRBench-3, a benchmark dataset from guideline development records for three diseases.
Conducted system-level testing with participants to evaluate efficiency and accuracy.

Main Results:

Quicker demonstrated precise question decomposition, expert-aligned evidence retrieval, and comprehensive screening.
Assisted data extraction improved accuracy; generated recommendations were more comprehensive and coherent than clinician-written ones.
System-level testing showed Quicker reduced recommendation development time to 20-40 minutes per participant.

Conclusions:

Quicker significantly enhances the speed and reliability of evidence-based clinical decision-making.
The LLM-powered system shows potential to streamline guideline development processes.
Quicker supports customized decision-making through integrated tools and interactive interfaces.