Ethical Considerations in Patient Privacy and Data Handling for AI in Cardiovascular Imaging and Radiology

Abstract

The integration of artificial intelligence (AI) into cardiovascular imaging and radiology offers the potential to enhance diagnostic accuracy, streamline workflows, and personalize patient care. However, the rapid adoption of AI has introduced complex ethical challenges, particularly concerning patient privacy, data handling, informed consent, and data ownership. This narrative review explores these issues by synthesizing literature from clinical, technical, and regulatory perspectives. We examine the tensions between data utility and data protection, the evolving role of transparency and explainable AI, and the disparities in ethical and legal frameworks across jurisdictions such as the European Union, the USA, and emerging players like China. We also highlight the vulnerabilities introduced by cloud computing, adversarial attacks, and the use of commercial datasets. Ethical frameworks and regulatory guidelines are compared, and proposed mitigation strategies such as federated learning, blockchain, and differential privacy are discussed. To ensure ethical implementation, we emphasize the need for shared accountability among clinicians, developers, healthcare institutions, and policymakers. Ultimately, the responsible development of AI in medical imaging must prioritize patient trust, fairness, and equity, underpinned by robust governance and transparent data stewardship.

Keywords: Artificial intelligence (AI); Blockchain in healthcare; Cardiovascular imaging; Data handling protocols; Data security; Ethical and legal considerations; Informed consent; Machine learning in medical imaging; Patient privacy; Radiology ethics.