Swarm-Based Clinical Validation Framework of Artificial Intelligence Solutions for Non-Communicable Diseases

Theofanis Fotis, Kitty Kioskli, Spyridon Papastergiou

    Research output: Contribution to journalArticlepeer-review

    Abstract

    Non-communicable diseases (NCDs) present complex challenges in patient care. Artificial Intelligence (AI) offers transformative potential, but its implementation requires addressing key issues. This study proposes a swarm intelligence-inspired clinical validation framework for NCDs, promoting openness, trustworthiness, and continuous self-validation. The framework creates a collaborative environment, connecting healthcare entities, patients, caregivers, and professionals. The swarm-based approach enhances diagnostic accuracy, enables personalized treatment, improves prognosis, supports clinical decision-making, engages patients, enables real-time monitoring, and promotes continuous learning. These implications have the power to revolutionize NCD management and improve patient outcomes.
    Original languageEnglish
    Pages (from-to)1-11
    Number of pages11
    JournalJournal of Engineering Research and Sciences
    Volume2
    Issue number9
    DOIs
    Publication statusPublished - 26 Sept 2023

    Keywords

    • Swarm Intelligence
    • Framework
    • Personalized Treatment
    • Clinical Validation
    • Diagnostic Accuracy
    • Non-Communicable Diseases

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