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Related Experiment Video

Updated: Jan 9, 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

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LEAD: Localized Explanations with Adversarial Decision Boundary Characterization for Interpretable Disease

Asiful Arefeen, Hassan Ghasemzadeh

    Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
    |December 3, 2025
    PubMed
    Summary
    This summary is machine-generated.

    LEAD, a new method, enhances model interpretability in digital health by explaining decisions using critical samples near the decision boundary. This improves trust and aids clinical decision-making for better patient outcomes.

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    Area of Science:

    • Artificial Intelligence
    • Digital Health
    • Machine Learning Interpretability

    Background:

    • Understanding AI decision-making is crucial in safety-critical fields like digital health.
    • Interpretability enhances trust, acceptance, and enables informed clinical decisions.

    Purpose of the Study:

    • Introduce LEAD, a novel method for localized feature explanations.
    • Improve the interpretability and robustness of AI models in healthcare.

    Main Methods:

    • LEAD generates explanations by perturbing adversarial critical samples near the sample to be explained.
    • Focuses on borderline instances along the decision boundary to reduce noise and enhance robustness.

    Main Results:

    • LEAD demonstrates improved fidelity (at least 6%) and consistency (at least 7%) compared to existing methods.
    • Achieves high sparsity and competitive robustness on physiological signal datasets.

    Conclusions:

    • LEAD offers effective localized feature explanations, boosting AI interpretability in digital health.
    • Enhances clinical decision-making by providing trustworthy insights for timely interventions.