Deep Learning and Optimization Approaches in Blockchain and Contextual White Shark Attention Network for Drug Supply Chain Management and Recommendations in the Smart Pharmaceutical Industry: A Review

Abstract

The rapid digital transformation of the pharmaceutical industry has increased the demand for intelligent technologies that enhance transparency, efficiency, and reliability in drug supply chains. These supply chains involve multiple stakeholders, including manufacturers, distributors, pharmacies, healthcare providers, and patients, making management highly complex. Blockchain technology has emerged as a robust solution by offering decentralized, tamper-resistant data storage and transparent transaction tracking, ensuring drug authenticity and traceability. It enables real-time monitoring of distribution processes while maintaining data integrity across all participants. Simultaneously, artificial intelligence and deep learning techniques are playing a vital role in improving decision-making and predictive analytics by analyzing large-scale healthcare data. These models help in demand forecasting, inventory optimization, and personalized drug recommendations. Optimization algorithms further enhance model performance, with the White Shark Optimizer (WSO) providing an effective nature-inspired approach for solving complex optimization problems through balanced exploration and exploitation. This review explores the integration of blockchain, deep learning, and optimization techniques, particularly Contextual White Shark Attention Networks, for improving pharmaceutical supply chain management and recommendation systems, while also highlighting current trends, challenges, and future research opportunities in this evolving interdisciplinary domain.