Audit: The Missing Layer in Healthcare AI Systems

This article discusses the importance of building comprehensive audit capabilities into healthcare AI systems, such as clinical decision support tools. It highlights the critical need for audit in high-stakes healthcare applications and outlines the key layers of audit required for compliance and safety.

Details

The article argues that in healthcare, a RAG (Retrieval, Analysis, Generation) system that occasionally hallucinates is not just an embarrassment, but a liability. The difference lies in whether comprehensive audit capabilities were built in from the start, rather than bolted on later. Audit in a healthcare RAG context must cover four key layers: input audit (logging who, what, and when), retrieval audit (logging source documents and scores), generation audit (logging model prompts and outputs), and decision audit (logging downstream actions and outcomes). This level of audit is required for compliance with regulations like HIPAA and the EU AI Act - 'doing your best' is not a valid defense. Audit also reveals real-world usage patterns that evaluation metrics alone cannot, allowing teams to identify and address critical gaps before they cause harm.