AI‑powered tools in hospitals are moving from pilots to operational use, rapidly changing the questions being asked by oversight bodies, regulators and the general public.

Like other components of a clinical process, these tools are expected to be evidence based, and this evidence should be clear and trusted. The hard part is that modern healthcare models learn from data that is rarely used “as is”. It is assembled through pipelines that normalise, harmonise and clean messy inputs, often across multiple systems and teams. Those upstream choices quietly shape what patterns an AI system can learn, and where it may fail. To maintain trust, healthcare organisations must be able to answer questions such as: where did the data come from, what was excluded, what changed since last month, how is drift monitored and who is accountable when outputs influence care? Today, software‑intensive upstream data processes often obscure this evidence and erode trust.

Standardising data processing and making it more transparent is no longer optional in Europe and is increasingly expected in other advanced healthcare systems. Regulatory frameworks such as the EU AI Act and EHDS will require organisations to demonstrate data governance, quality management and fitness-for-use in ways that support oversight, accountability and reuse over time.  Standards bodies are beginning to describe complex data pipelines, but current approaches still do not scale because they produce narrative artefacts that are difficult to inspect, compare or use as evidence in decision-making.

In this session we take that a step further and introduce a complementary approach to trust: instead of reconstructing data histories from documents, we can capture transformations automatically as workflows execute, creating structured, queryable records. Instead of manual compliance checks, we can express key requirements as formal, verifiable rules that run over those records. Drawing on real‑world implementations in large‑scale claims data pipelines (including Medicare) and trusted research environments, we will show how this approach helps clinical and programme leaders understand what was actually done to data, not just what should have been done, enabling them to approve deployments with genuine confidence and scale AI responsibly.

A copy of the book that informed this perspective will be reserved for the audience question that most constructively advances the discussion.