AI-Based Drug Quality Surveillance and Counterfeit Medicine Detection

Abstract

Substandard and counterfeit medicines are becoming one of the serious threats to global health care, affecting effectiveness of treatment and causing adverse reactions due to compromised quality of medicines. Traditional approaches to detection of counterfeit medicines are often inefficient due to excessive complexity of pharmaceutical supply chains and lack of automation. Thus, this study aims to assess the efficiency of application of Artificial Intelligence in drug quality surveillance and detection of counterfeit medicines through comparison of various machine learning and deep learning algorithms. Quantitative comparative research methodology has been used in this study, including pharmaceutical data consisting of 15,000 samples of both authentic and counterfeit medicines. Evaluation has been performed based on accuracy, precision, recall, F1-score, false positive rate and processing time of four AI-based algorithms, including Random Forest, Support Vector Machine, Extreme Gradient Boosting and Convolutional Neural Network (CNN). According to obtained results, the CNN algorithm has shown the highest accuracy of detection equal to 99.3%, with 99.1% precision, 98.9% recall and 99.0% F1-score, significantly outperforming all other algorithms. AI-based surveillance system decreased the time required for medicine inspection by 81.7% and decreased the amount of human error by 79.5% in comparison with traditional methods. The study also shows that intelligence surveillance increases transparency of pharmaceutical supply chains, enhances medicine authentication process, promotes pharmacovigilance and assists regulatory decision-making by automatic detection of anomalies in pharmaceutical supply chains. Despite certain challenges concerning data quality, regulatory compliance, cybersecurity and costs associated with implementation, the research concludes that AI offers an efficient and reliable solution for pharmaceutical quality assurance. Wider use of AI-based surveillance systems may help to decrease the number of counterfeit medicines, improve patient safety and strengthen healthcare governance.