Ensuring patient data privacy while using AI for disease diagnosis requires implementing robust measures at every stage of data collection, storage, and processing. Techniques such as data anonymization and encryption can protect sensitive information by removing identifiable elements and securing data against unauthorized access. Federated learning is another promising approach, where AI models are trained on decentralized data across multiple locations without sharing the actual patient data. Additionally, strict access controls, audit trails, and compliance with regulations like GDPR or HIPAA ensure that data is only accessed and used by authorized personnel for legitimate purposes. Educating healthcare professionals and AI developers on ethical data handling practices further reinforces privacy safeguards. By combining technological solutions with regulatory frameworks and ethical practices, patient data privacy can be maintained while leveraging AI's potential to improve disease diagnosis and healthcare outcomes.