Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Experiment Video

Updated: Apr 15, 2026

Author Spotlight: An Efficient and Robust Software for Automated Fusion of Multiple Preclinical Imaging Modalities
07:13

Author Spotlight: An Efficient and Robust Software for Automated Fusion of Multiple Preclinical Imaging Modalities

Published on: October 27, 2023

1.8K

XMedFuse: An Explainable Multimodal Feature Fusion Framework for Healthcare Diagnostics.

Keyan Li, Daohua Pan, Bander A Alzahrani

    IEEE Journal of Biomedical and Health Informatics
    |April 13, 2026
    PubMed
    Summary
    This summary is machine-generated.

    Related Concept Videos

    Classification of Illness01:17

    Classification of Illness

    9.4K
    The meaning of illness is individualized to each person who experiences an alteration in health. In contrast, disease is a medical term indicating a pathological change in the structure and function of the body or mind. It is a condition that has specific symptoms and boundaries.
    An illness is a response to a disease in which the person's level of functioning is changed compared with a previous level. The general classification of illness includes acute and chronic.
    Acute illness is severe...
    9.4K

    You might also read

    Related Articles

    Articles linked to this work by shared authors, journal, and citation graph.

    Sort by
    Same author

    Exploratory Pilot Multi-Omics Profiling of Gut Microbiota and Metabolic Features in Patients with Prolactinoma.

    Cancer management and research·2026
    Same author

    Earthworm-Inspired Self-Powered Multistimuli Neuromorphic Vision Skin with Homogeneous Ion Heterogel Arrays.

    ACS applied materials & interfaces·2026
    Same author

    A Biomimetic Self-Adaptive Neurovision Eye With an Integrated Gel Iris and Retinamorphic Architecture.

    Small (Weinheim an der Bergstrasse, Germany)·2026
    Same author

    The onco-functional reorganization of language network underlying metaplasticity induced by gliomas.

    Frontiers in oncology·2026
    Same author

    Development of diagnostic criteria for Behçet's uveitis in a Chinese population.

    The British journal of ophthalmology·2026
    Same author

    A Real-world Pharmacovigilance Study of the FAERS Database: Safety Signals of Common Medications for Non-infectious Uveitis.

    Current molecular medicine·2026
    Same journal

    An Ultra-Lightweight Cross-scale Attention Mamba Network for Accurate Skin Lesion Segmentation.

    IEEE journal of biomedical and health informatics·2026
    Same journal

    Explanation-Guided Reconstruction of Missing Clinical Features for Survival Prediction in Pancreatic Cancer.

    IEEE journal of biomedical and health informatics·2026
    Same journal

    stDGCN: A dual-augmentation graph convolutional network for identifying spatial domains with attention mechanism.

    IEEE journal of biomedical and health informatics·2026
    Same journal

    Patient-specific Biomechanical Investigation of Percutaneous Pulmonary Valves: Towards the Integration of Routinely Acquired Clinical Data and Fluid-structure Interaction Simulations.

    IEEE journal of biomedical and health informatics·2026
    Same journal

    Cross-subject fMRI-to-Image with Visual-cortex 2D Representation and Pre-Training.

    IEEE journal of biomedical and health informatics·2026
    Same journal

    PGCASurv: A Prior-Guided Cross-Attention Framework for Dynamic Survival Model with Longitudinal Data.

    IEEE journal of biomedical and health informatics·2026
    See all related articles

    This study introduces XMedFuse, an explainable AI framework for medical diagnostics. It efficiently fuses physiological signals for accurate, interpretable clinical decisions on edge devices.

    Area of Science:

    • Artificial Intelligence in Medicine
    • Biomedical Signal Processing
    • Machine Learning for Healthcare

    Background:

    • AI enhances clinical decision support but often lacks medical reasoning and efficiency for edge devices.
    • Existing AI models struggle with interpretability and computational demands in healthcare 4.0.
    • Need for explainable, efficient AI solutions for real-time medical diagnostics on resource-constrained devices.

    Purpose of the Study:

    • To develop XMedFuse, an explainable multimodal feature fusion framework for AI-driven clinical decision support.
    • To integrate morphological and temporal features from physiological signals using a lightweight architecture.
    • To ensure AI recommendations are interpretable and align with medical reasoning for clinical validation.

    Main Methods:

    Related Experiment Videos

    Last Updated: Apr 15, 2026

    Author Spotlight: An Efficient and Robust Software for Automated Fusion of Multiple Preclinical Imaging Modalities
    07:13

    Author Spotlight: An Efficient and Robust Software for Automated Fusion of Multiple Preclinical Imaging Modalities

    Published on: October 27, 2023

    1.8K
    • Proposed XMedFuse framework using depthwise separable convolutions and dense connectivity for efficient feature extraction.
    • Integrated morphological (waveform) and temporal (rhythm) signal pathways with adaptive fusion.
    • Employed gradient-weighted attribution mapping for interpretable decision visualizations.

    Main Results:

    • Achieved 98.4% internal and 96.7% external testing accuracy on cardiovascular diagnostic tasks.
    • Demonstrated high efficiency (48.2K parameters) and robustness in noisy conditions (93% accuracy at 15dB SNR).
    • Interpretability analysis showed 94% alignment between model attention and clinical criteria, confirming medical relevance.

    Conclusions:

    • XMedFuse offers a highly accurate, efficient, and interpretable AI solution for clinical decision support on edge devices.
    • The framework's explainability enables clinicians to validate AI-driven diagnostic recommendations against medical knowledge.
    • XMedFuse represents a significant advancement towards transparent and reliable AI deployment in modern healthcare systems.