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Hospitals, health systems, pharmaceutical companies, medtech and research organisations.
Healthcare & Life Sciences
Clinical and research AI where safety, provenance and validation evidence are inseparable.
The challenge
What makes governance hard here.
- Clinical and research AI must be validated, monitored and explainable in a safety-critical context.
- Data provenance and consent constraints determine what AI is permitted to do.
- Quality, regulatory and technology governance operate under different systems of record.
How Regurai helps
What changes with a connected model.
- Link AI systems to the clinical or research processes they support and the controls that supervise them.
- Track data provenance, sensitivity and permitted purpose behind each AI capability.
- Hold validation, monitoring and change evidence against the system it belongs to.
Use cases
Decisions Regurai supports in this sector.
Assessing risk before introducing AI into a clinical or research pathway
Tracing the data lineage behind a diagnostic or research model
Understanding the impact of a technology or supplier change on clinical services
Maintaining validation and change evidence for quality review
Relevant capabilities
Capabilities most used here.
- AI & Model Inventory
- Data Lineage & Sensitivity
- Policy & Control Library
- Scenario Simulation
- Audit Trail & Evidence
Governance considerations
What oversight typically focuses on.
- Patient safety and clinical oversight of AI-assisted decisions
- Data provenance, consent and sensitivity constraints
- Validation, change control and quality evidence