Ensuring Data Integrity and Security in AI-Based Lung Cancer Nodule Classification and Detection in CT Imaging Systems

Artificial intelligence (AI) in CT imaging for identifying and categorizing lung cancer is now considerably more efficient and accurate. Deep learning algorithms used in AI-driven models help to detect lung nodules, hence improving early detection and lowering human error. Still, maintaining data integrity and security is a difficult task that might affect the dependability and credibility of many artificial intelligence systems. Model performance and patient confidentiality are threatened by adversarial attacks, data poisoning, and privacy violations. The present study investigates several methods to protect artificial intelligence-driven lung nodule classification systems from adversarial manipulations, data corruption, and illegal access. It looks at security improving approaches like adversarial defensive mechanisms, federated learning, and encryption based privacy-preserving technologies and data integrity strategies, including blockchain-based data management, strong preprocessing, and standardized annotations protocols. The study's objective is to overcome these challenges, which enable the growth of AI applications in medical imaging that are safe, reliable, and ethically aware.