Artificial Intelligence Techniques for Blockchain and Contextual White Shark Attention Network for Drug Supply Chain Management and Recommendations in the Smart Pharmaceutical Industry: Trends and Challenges

Abstract

The pharmaceutical supply chain is essential for ensuring the safe, efficient, and reliable distribution of medicines across healthcare systems, yet it faces challenges such as complex logistics, limited transparency, and the growing threat of counterfeit drugs. Emerging technologies like blockchain and artificial intelligence (AI) offer promising solutions to these issues. Blockchain provides decentralized and immutable data storage, enhancing traceability, security, and transparency by recording drug manufacturing, distribution, and delivery processes, allowing stakeholders to verify authenticity and prevent fraud. Simultaneously, AI techniques, including deep learning and attention-based neural networks, are widely used to analyze healthcare data and improve drug recommendation systems. Contextual attention networks enable models to focus on relevant features, leading to more accurate predictions and personalized recommendations. Additionally, optimization techniques such as the White Shark Optimizer improve neural network performance by efficiently tuning model parameters and enhancing convergence. This review explores the integration of blockchain with AI-driven attention networks for pharmaceutical supply chain management and recommendation systems, highlighting key architectures, challenges, and opportunities. The findings indicate that such integrated approaches significantly improve transparency, predictive analytics, and decision-making, while future research should address scalability, security, and explainable AI for smart pharmaceutical ecosystems.