Clinical AI Governance in Hospitals: Why Healthcare Needs an AI System Registry

Artificial intelligence is increasingly being deployed across healthcare systems. Hospitals are experimenting with AI applications in radiology, pathology, clinical decision support, workflow optimisation, and patient triage.

These technologies promise improvements in efficiency and diagnostic accuracy, but they also introduce new governance challenges. Many healthcare organisations currently lack structured mechanisms to track which AI systems are deployed, how they were validated, and how they are monitored once they enter clinical workflows.

As the adoption of AI expands, the need for clinical AI governance infrastructure becomes increasingly apparent.

Unlike traditional medical devices, many AI systems evolve over time. Algorithms may be retrained, updated, or deployed across multiple clinical contexts. This creates challenges for healthcare organisations that must ensure transparency, safety, and accountability.

Common governance questions arise:

• —Which AI systems are currently deployed in the hospital?
• —Who approved their clinical use?
• —What validation evidence supports their use?
• —How are these systems monitored after deployment?

In many institutions, the answers to these questions are scattered across spreadsheets, committee documents, or internal reports. Without a structured governance framework, oversight becomes fragmented and difficult to maintain.

One emerging solution is the concept of a clinical AI registry.

A clinical AI registry provides a structured inventory of AI systems deployed in healthcare environments. Each system can be documented with key governance information, such as:

• —clinical application and intended use
• —vendor or developer information
• —validation studies and evidence
• —risk classification
• —governance approval decisions
• —lifecycle monitoring data

By maintaining a centralised registry, hospital leadership and governance boards gain visibility over the AI systems influencing clinical workflows.

Registries have long been used in healthcare for tracking patient outcomes and medical devices. Applying the same model to clinical AI governance offers a practical mechanism for documenting and overseeing the deployment of AI technologies.

Regulatory developments are also increasing the importance of governance structures.

The European Union has introduced a risk-based framework for artificial intelligence systems through the EU Artificial Intelligence Act. Many healthcare AI systems fall into categories considered high-risk, particularly where AI systems support clinical decision-making.

High-risk systems are expected to meet requirements related to:

• —documentation
• —transparency
• —risk management
• —human oversight
• —monitoring after deployment

Healthcare organisations therefore need governance mechanisms capable of maintaining structured records of AI systems and their validation evidence.

A registry approach can support this requirement.

A typical clinical AI governance workflow may include several stages:

1. AI system registration

Hospitals document the AI system, its intended clinical use, and relevant vendor information.

2. Validation documentation

Evidence supporting the system's clinical validity and safety is recorded.

3. Governance review

Oversight committees assess whether the system can be deployed in clinical workflows.

4. Monitoring and lifecycle oversight

The system is monitored over time, including updates, retraining events, and performance outcomes.

A registry platform allows these governance steps to be documented in a structured way.

To explore governance infrastructure for healthcare AI systems, the Clinical AI Registry™ initiative provides tools for documenting and monitoring clinical AI deployments.

The registry concept focuses on providing healthcare organisations with infrastructure for:

• —maintaining an inventory of clinical AI systems
• —documenting validation evidence
• —recording governance review decisions
• —supporting lifecycle monitoring of deployed AI technologies

Further information about the initiative can be found at clinicalairegistry.eu.

The project aims to explore practical governance models for hospitals seeking to manage AI systems responsibly within clinical environments.

Artificial intelligence is likely to become a permanent part of healthcare systems. As AI applications expand across clinical domains, governance mechanisms will need to evolve accordingly.

Hospitals will require structured oversight frameworks capable of documenting AI systems, tracking validation evidence, and maintaining transparent governance processes.

A registry-based approach offers a practical foundation for these governance structures.

The challenge ahead is not only to develop innovative AI technologies, but also to ensure that they are deployed responsibly within healthcare systems that prioritise safety, accountability, and transparency.

For healthcare organisations interested in exploring governance infrastructure for clinical AI, Lifecycle Governance™ provides additional resources and frameworks supporting responsible AI deployment in healthcare environments.