AI-driven Pharmacy Traceability and Adulteration Detection System for Medicines

Thesis Abstract

This study addresses the growing threats of counterfeit medicines and opaque supply chains by developing an AI-driven system for end-to-end pharmacy traceability and adulteration detection that integrates digital authentication, real-time monitoring, and predictive analytics. The aim is to reduce illicit supply routes, improve quality assurance, and enhance patient safety through transparent provenance and rapid anomaly detection. Specific objectives are to (i) design a hybrid AI architecture combining blockchain-enabled provenance with machine learning-based anomaly detection, (ii) evaluate an optical and chemical fingerprinting module for in-situ adulteration screening, (iii) validate the system’s performance in real-world pharmacy networks across three major urban centers, and (iv) assess stakeholder perceptions and organizational readiness for deployment. The study adopts a mixed-methods approach underpinned by the legitimacy theory and technology acceptance models to examine trust, perceived usefulness, and governance implications. Methodologically, a convergent parallel design will be employed. The population comprises licensed pharmacies, distribution centers, and regulatory bodies within a metropolitan pharmaceutical network. A stratified random sample of 60 pharmacies, 12 distributors, and 6 regulatory offices will be recruited, with data collection spanning twelve months. Data sources include (1) operational data from the AI platform (traceability logs, authentication events, and adulteration alerts), (2) laboratory analyses of sampled medicines using high-performance liquid chromatography (HPLC) and mass spectrometry (MS) for chemical fingerprinting, and (3) qualitative data from semi-structured interviews with pharmacists, supply chain managers, and regulators. Instrumentation will include a standardized traceability dashboard, an adulteration detection module employing convolutional neural networks (CNNs) for spectral image analysis and anomaly detection via one-class support vector machines (OC-SVM), and a survey instrument adapted from established technology acceptance models. Validity and reliability will be established through pilot testing, calibration with certified reference standards, back-translation where applicable, and test-retest reliability analyses for survey measures. Data analysis will proceed in three streams. Quantitative data from system logs, lab results, and surveys will be analyzed using regression analysis to identify predictors of detection accuracy and time-to-detection, ANOVA to compare performance across different network segments, and receiver operating characteristic (ROC) analysis to evaluate adulteration detection sensitivity and specificity. Time-series analyses will examine trends in counterfeit events pre- and post-implementation. Qualitative data from interviews will be thematically analyzed to uncover determinants of adoption, governance challenges, and perceived impact on patient safety, with coding corroborated by member checking. A conceptual model illustrating the interrelationships among provenance integrity, AI-driven anomaly detection, laboratory verification, and stakeholder trust will be proposed and tested using structural equation modelling (SEM) where sample size permits. Ethical considerations include data privacy, informed consent, data minimization, and compliance with pharmaceutical regulations. Expected findings anticipate high authentication accuracy (>95%) for verified products and robust adulteration detection performance (true positive rate >0.90, false positive rate <0.05) when integrating spectral fingerprinting with AI-based anomaly detection. The system is expected to reduce time-to-detection of adulterated medicines by 40–60% and improve traceability coverage to 99% of registered supply chain events. The study will identify critical success factors for implementation, including data interoperability standards, governance protocols for smart-contract-based provenance, and alignment with regulatory requirements. The contribution to knowledge includes a novel integrative framework that combines blockchain-enabled provenance with multimodal AI for real-time adulteration surveillance in pharmaceutical supply chains, empirical evidence on system performance in real-world settings, and actionable guidelines for policymakers and industry practitioners. The main conclusion anticipates that AI-driven traceability coupled with rapid adulteration screening can substantially enhance medicine quality assurance and patient safety, provided that governance, data standardization, and stakeholder engagement are effectively managed. Recommendations include establishing harmonized data exchange standards, scaling the platform to national networks, continuous model retraining with new spectral libraries, and ongoing training for personnel to sustain trust and operational efficacy.