Artificial intelligence (AI) has the potential to revolutionise healthcare by improving diagnostics, treatment options, and operational efficiencies. However, for AI to reach its full potential in this sensitive industry, several critical challenges must be addressed. These challenges include ensuring the availability of high-quality data, adhering to complex regulatory frameworks, and managing the risks inherent in deploying AI systems.

This whitepaper will teach you best practices for managing AI risks and quality in healthcare—walking you through three key areas:

• Data Quality and Structuring: Understanding how to structure and manage high-quality healthcare data for AI use is essential. Learn best practices for securing data flows, ensuring compliance, and enabling federated data for AI system training.

• Regulatory Compliance: Healthcare regulations like GDPR and the EU AI Act demand rigorous oversight. This section provides a guide on how to navigate compliance complexities, ensuring your AI systems meet global standards.

• AI System Lifecycle Management: From training data to inference and deployment, refining your AI system throughout its lifecycle helps mitigate risks and ensures ongoing performance. Explore actionable insights into system testing, validation, and monitoring.