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Related Experiment Video

Updated: Jul 4, 2026

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems

Published on: June 13, 2025

Exploring Synergies Between Large Language Models and Knowledge Models in Healthcare: A Scoping Review.

Lamine Youbi¹, Akram Redjdal², Brigitte Seroussi^1,3

¹Sorbonne Université, INSERM, Université Sorbonne Paris Nord, LIMICS, Paris, France.

Studies in Health Technology and Informatics

|July 3, 2026

Summary

This summary is machine-generated.

Integrating Large Language Models (LLMs) with knowledge models (KMs) enhances clinical decision support. This synergy improves AI reliability, explainability, and safety in healthcare applications.

Keywords:

Clinical Decision Support Systems Knowledge Graphs Large Language Models Retrieval-Augmented Generation (RAG)

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Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems

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Published on: December 6, 2024

Area of Science:

Artificial Intelligence in Medicine
Clinical Decision Support Systems
Knowledge Representation and Reasoning

Background:

Large Language Models (LLMs) and knowledge models (KMs) are increasingly vital for advancing clinical decision support systems.
Understanding the synergistic potential between LLMs and KMs is crucial for optimizing healthcare AI.

Purpose of the Study:

To conduct a scoping review examining the integration of LLM and KM approaches.
To explore the synergy in knowledge model construction, enrichment, and LLM optimization.

Main Methods:

Scoping review of studies integrating LLMs and KMs.
Categorization of studies into knowledge-grounded reasoning, LLM-driven knowledge model engineering, and hybrid approaches.

Main Results:

Retrieval-augmented generation (RAG) using KMs enhances LLM reliability, explainability, and adherence to clinical guidelines.
LLMs effectively automate knowledge model development tasks like named entity recognition and relation extraction.
Hybrid frameworks combining LLMs and KMs show improved performance and safety.

Conclusions:

The integration of LLMs and KMs leads to more reliable, scalable, and interpretable AI systems for healthcare.
Synergistic approaches are key to advancing AI in clinical decision support.
Future research should focus on developing and validating hybrid LLM-KM frameworks.