Clinical performance
Are outputs accurate, complete, and appropriate for the clinical context?
Independent assurance
for AI operating in healthcare.
TalentFirst evaluates AI in clinical workflows to build the evidence needed for safer deployment, ongoing monitoring, and governance review.
ModelCapabilities
SystemTools & context
WorkflowDecisions & oversight
DeploymentReal-world conditions
The evidence cycle
Reliability has to hold
after deployment.
A predeployment evaluation establishes a baseline. Changes in cases, users, integrations, and system versions can affect performance inside the workflow. Monitoring shows where and when the system is regressing, and what needs to be reviewed or retested.
Evidence across the
system lifecycle.
system lifecycle.
01
Evaluate before deployment
Establish expected behavior, failure modes, and human review requirements for the workflow.
What we evaluate
Know what requires attention.
Evidence should reflect how the system performs inside the clinical workflow, including the decisions it influences and the people expected to oversee it.
Workflow behavior
Does the system behave correctly across the full sequence of work?
Actions and tools
Are actions permitted, traceable, and completed through the right systems?
Human oversight
Does the right person review the right decision at the right time?
Failures and incidents
Can important failures be detected, investigated, and converted into evidence?
Changes over time
Do model, workflow, or data changes introduce new risks after deployment?
Research notes
Methods for evaluating healthcare AI in real workflows.
Technical notes on workflow evaluation, monitoring, human oversight, and the evidence needed as systems change.
Human oversight
When the Approval Button Is Theatre
What effective human review requires.
Read article Regression evaluationRegression Testing for AI: Gates That Actually Block a Release
How regression tests can inform release decisions.
Read article Evaluation dataBuilding Golden Datasets That Don’t Rot
How evaluation datasets can remain useful as systems change.
Read articleFor healthcare organizations
Evaluate AI for a real clinical workflow.
Define the deployment context, expected behavior, failure modes, monitoring requirements, and evidence needed for review.
For teams building healthcare AI
Request data for training or evaluation.
Describe the model, task, domain expertise, and data requirements. Requests are scoped against available expertise and assets.
Discuss your data needs