Deep Learning and Optimization Approaches in Blockchain-Based Hybrid Contextual-ATNet Approach for Daily Diabetes Management: Predicting Insulin Dosage for Improved Control: A Review

Abstract

The pharmaceutical supply chain is a highly complex and critical system requiring transparency, reliability, and regulatory compliance to ensure drug safety and timely delivery. Traditional supply chain models are vulnerable to issues such as counterfeit drugs, cold chain failures, and data manipulation, posing significant risks to patient safety and global healthcare systems. These challenges necessitate advanced technological solutions capable of improving traceability, integrity, and operational efficiency. This paper presents a comprehensive review of integrated technologies, focusing on blockchain, finite element methods (FEM), and neural network-based modeling for pharmaceutical supply chain management. Blockchain provides a decentralized and immutable framework for secure data sharing and traceability, while FEM enables accurate modeling of physical conditions such as temperature, stress, and material behavior during transportation and storage. The integration of FEM with neural networks allows real-time predictive modeling of drug stability and packaging integrity. Applications include cold chain monitoring, counterfeit detection, and regulatory compliance within smart supply chain systems. The review also highlights enabling technologies such as IoT, RFID, and digital twins for real-time data acquisition and system optimization. While these integrated approaches demonstrate improved transparency, efficiency, and predictive capability, challenges related to scalability, computational cost, and system integration remain, emphasizing the need for future research in intelligent and resilient pharmaceutical supply chain systems.