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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