Developing AI-Based Systems for Detecting and Preventing Fraud in Nuclear Medicine Supply Chains

The integrity of nuclear medicine supply chains is critical to ensuring the availability of lifesaving diagnostic and therapeutic tools. However, these supply chains are increasingly vulnerable to fraud, including counterfeit pharmaceuticals, unauthorized distribution, and financial mismanagement. This study explores the development of AI-based systems for fraud detection and prevention within nuclear medicine supply chains. Leveraging advanced machine learning algorithms, natural language processing, and anomaly detection models, the proposed framework integrates real-time monitoring, predictive analytics, and blockchain-based traceability to enhance transparency and security. By incorporating domain-specific datasets and explainable AI techniques, the system aims to identify fraud patterns, mitigate risks, and facilitate compliance with regulatory standards. This research underscores the transformative potential of artificial intelligence in safeguarding the complex and sensitive supply chains of nuclear medicine.