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  3. Medicare: Medical Collaborative Agents Reasoning Over Interpretable Heterogeneous Graphs.
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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: June 13, 2025

MediCARE: Medical Collaborative Agents REasoning over Interpretable Heterogeneous Graphs.

Antonino Ferraro1, Antonio Galli2, Valerio La Gatta3

  • 1Department of Information Science and Technology, Pegaso University, Piazza Trieste e Trento 48, Naples, 80132, Italy.

Artificial Intelligence in Medicine
|May 12, 2026

View abstract on PubMed

Summary
This summary is machine-generated.

This study introduces the MediCARE framework, using AI to improve medical reports. It combines predictive models with Large Language Models (LLMs) to reduce errors and enhance accuracy in healthcare recommendations.

Keywords:
Collaborative LLMsGraph Neural NetworksLarge Language ModelsPersonalized medicineeXplainable Artificial Intelligence

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Area of Science:

  • Artificial Intelligence in Medicine
  • Medical Informatics
  • Machine Learning for Healthcare

Background:

  • Large Language Models (LLMs) show promise as medical reasoning agents.
  • LLM hallucinations present a significant challenge in the medical domain, risking inaccurate patient information.
  • Existing AI approaches require robust methods to ensure reliability and interpretability in clinical decision support.

Purpose of the Study:

  • To propose and evaluate the Predict→Interpret→Explain (PIE) paradigm for reliable medical AI.
  • To develop a framework (MediCARE) that integrates Graph Neural Networks (GNNs) with LLMs for enhanced medical reporting.
  • To mitigate LLM hallucinations and improve the precision of AI-driven medical recommendations.

Main Methods:

  • Utilized graph data structures to represent patient clinical data for Graph Neural Network (GNN) modeling.
  • Employed eXplainable AI (XAI) techniques, including GNNExplainer and Integrated Gradients, for model interpretation.
  • Leveraged a pool of LLM-based collaborative agents to process predictions and generate comprehensive medical reports.

    • Main Results:

    • The MediCARE framework demonstrated the capability of LLMs in generating accurate medical reports.
    • The integration of LLMs and GNNs led to improved prediction precision in medication recommendation tasks.
    • The PIE paradigm effectively addressed the challenge of LLM hallucinations in a medical context.

    Conclusions:

    • The MediCARE framework, based on the PIE paradigm, offers a promising approach to reliable AI in medicine.
    • LLMs can be effectively utilized to enhance the accuracy and comprehensibility of AI-generated medical assessments.
    • This study highlights the potential of combining predictive modeling, XAI, and LLMs for safer clinical decision support systems.