Discover Artificial Intelligence

Abstract Influence maximization (IM) aims to select a set of influential nodes in social networks to maximize information diffusion. However, existing methods struggle to balance solution accuracy and computational efficiency, restricting their practical deployment as network size increases. This paper proposes a Discrete Dung Beetle Optimizer for the IM problem. We discretize the Dung Beetle Opt…

Abstract Accurate classification of clinically significant (CS) versus clinically insignificant (CiS) prostate cancer is critical for treatment planning, yet clinical adoption of AI-based diagnostic systems remains limited by two fundamental barriers: achieving clinically meaningful performance with balanced sensitivity/specificity for decision support, and lack of transparent decision-making tha…

Artificial intelligence has become a central component of national competitiveness, yet most global assessments of AI readiness rely on static, cross-sectional benchmarks that obscure the dynamic and path-dependent processes through which capability develops, diverges, and stabilizes over time. This study reconceptualizes AI readiness as an evolving system and addresses three questions: the traje…

Abstract AI-enabled tools increasingly influence clinical decisions, patient journeys, and digital health services, yet many deployments struggle with opaque decision-making, fragmented governance, and limited engagement with clinicians and patients. This article presents a structured narrative review and conceptual capability-centric framework for responsible healthcare AI that treats AI not as …

Abstract The Neural Network Integrity Constraint (NNIC) approach is a novel technique that aims to improve the accuracy of neural network (NN) classifications by reducing misclassified instances using integrity constraints (ICs) derived from a held-out residual dataset (separate from the final test set). This ensures a leakage-free evaluation. The paper describes the NNIC approach and evaluates t…

Abstract Generative Artificial Intelligence (GenAI) has evolved into a transformative technology whose unprecedented growth and public exposure have revealed challenging issues ranging from privacy protection to reducing factual inaccuracies and hallucinations, model security risks, legal complications, and a lack of interpretability. This position paper examines how Differential Privacy (DP), a …

Abstract Artificial intelligence (AI) is ushering in a transformative era in laboratory diagnostics, addressing many limitations of traditional workflows characterized by manual processes, delays, and susceptibility to human error. This review provides valuable insights for clinicians, researchers, and policymakers, emphasizing the importance of responsible and evidence-based deployment of AI to …