Governing Healthcare AI at Speed: What Changes When Privacy Is Enforced by Design
Blog post from Duality
Privacy in healthcare has become foundational to patient trust and the lawful use of data, especially as AI becomes integral to diagnostics and health operations. The challenge lies in deploying AI without violating patient protection rules, a process often delayed by lengthy approval procedures involving data protection assessments and ethics reviews. However, privacy-enhancing technologies (PETs) such as federated learning and differential privacy are transforming these processes by embedding governance directly into AI systems, reducing risks traditionally associated with compliance. A case study involving NHS England and the National Cancer Institute demonstrates how PETs cut approval times significantly by keeping patient data local and secure, aligning with frameworks like the NIST AI Risk Management Framework. This shift from reactive to proactive compliance allows healthcare organizations to use sensitive data securely, promoting innovation without compromising on privacy standards. As healthcare data grows in volume and value, federated ecosystems supported by PETs offer a scalable and trustworthy model for AI development, positioning organizations that adopt these technologies as leaders in compliant AI.
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