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Updated: Jul 1, 2026

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems
05:47

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

Published on: June 13, 2025

RE-LIG: A Faithfulness-Driven Layer Integrated Gradients Framework for Explainable Medical Visual Question Answering.

Esra Balık1, İrfan Aygün2, Mehmet Kaya3

  • 1Department of Software Engineering, Bandırma Onyedi Eylül University, Bandırma, Türkiye.

Journal of Imaging Informatics in Medicine
|June 29, 2026
PubMed
Summary
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This study introduces a high-resolution framework (RE-LIG) to improve the explainability and reliability of Medical Visual Question Answering (Med-VQA) systems, enhancing clinical decision-making.

Area of Science:

  • Artificial Intelligence
  • Medical Imaging
  • Explainable AI

Background:

  • Medical Visual Question Answering (Med-VQA) systems aid clinical decisions but suffer from limited transparency and low-resolution issues.
  • Existing 'black-box' models hinder clinical applicability due to a lack of explainability.

Purpose of the Study:

  • To propose a high-resolution holistic framework, Robust and Efficient Layer-Integrated Gradients (RE-LIG), to enhance reliability and explainability in Med-VQA.
  • To improve the transparency and trustworthiness of Med-VQA systems for clinical use.

Main Methods:

  • Developed a high-resolution framework integrating PubMedCLIP for visual encoding and BioLinkBERT for semantic fusion via coattention.
  • Incorporated the RE-LIG algorithm, combining noise tunneling and layer-based integration, to overcome limitations of traditional gradient-based explainability methods.
Keywords:
Explainable artificial intelligence (XAI)Medical VQAMultimodal learningRE-LIGVision transformers

Related Experiment Videos

Last Updated: Jul 1, 2026

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems
05:47

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

Published on: June 13, 2025

  • Utilized dynamic positional embedding interpolation for high-resolution visual encoding and a coattention mechanism for multimodal semantic fusion.

    • Main Results:

    • The RE-LIG framework significantly increased model faithfulness, achieving +28.9% higher explanation fidelity compared to standard gradient approaches (RE-LIG AOPC=0.3180 vs. Vanilla IG=0.2467).
    • The system maintained competitive performance with state-of-the-art models, showing 80.77% overall accuracy, 87.61% closed-ended, and 77.34% open-ended accuracy.
    • Ablation studies confirmed that noise reduction mechanisms improved focus on pathological boundaries, demonstrating explainability as a verifiable requirement.

    Conclusions:

    • The proposed RE-LIG framework enhances explainability and reliability in Med-VQA systems without compromising diagnostic performance.
    • Explainability in Med-VQA is a measurable and verifiable requirement, crucial for clinical confidence and decision-making.
    • The RE-LIG framework offers a promising solution for transparent and trustworthy AI in medical image interpretation.