Before healthcare AI meets patients, test it against clinical reality.
Independent clinical review for healthcare AI teams. Identify patient-safety risks, workflow failures, critical edge cases and human–AI interaction problems before pilot or deployment.
A technically correct AI system can still fail clinically.
Accuracy alone does not tell you whether an AI-supported workflow is safe, understandable or useful when it reaches real clinicians and real patients.
Clinical AI Readiness Review
A focused independent review of one AI-supported healthcare workflow before pilot, procurement discussion or wider implementation.
Clinical failure modes
Situations where apparently reasonable AI output could become clinically misleading, incomplete or unsafe.
Workflow fit
How the AI function interacts with the actual work of nurses and other healthcare professionals.
Patient-safety risks
Risks related to missed deterioration, misplaced priority, incomplete information and inappropriate reliance on AI.
Edge cases
Contradictory information, unusual situations, interruptions, missing inputs and uncertain responsibility.
Human–AI interaction
Automation bias, interpretability, source transparency, uncertainty and escalation.
Prioritised recommendations
Clear findings showing what should be addressed, mitigated or retested before broader use.
One workflow. Three steps. Clear findings.
The review is intentionally focused so it can be used before a pilot without becoming another long assessment process.
Show the workflow
Provide a demo, prototype, test environment or walkthrough of the AI-supported clinical workflow.
Clinical stress-test
The workflow is reviewed against realistic clinical situations, uncertainty, information gaps and patient-safety failure modes.
Readiness report
Receive prioritised findings explaining what should be addressed, tested or clarified before broader use.
Correct output is only the beginning.
AI-assisted clinical summary
Imagine an AI system that summarises information before a nurse assesses a patient receiving healthcare at home.
A conventional product test may ask:
- Is the summary factually correct?
- Is the language clear?
- Were important facts omitted?
- Does the system respond consistently?
A clinical readiness review also asks:
- Did the AI recognise the clinically important change?
- Were observations from different sources combined appropriately?
- Is uncertain information presented as certain?
- Can the clinician trace important claims back to their source?
- Could the output create inappropriate reassurance?
- Should this situation trigger escalation rather than summarisation?
Start with one real workflow.
A defined Clinical AI Readiness Review is designed to find clinically important problems while they are still relatively inexpensive to fix.
Broader product reviews and multiple workflows can be scoped separately.
excl. VAT
Independent clinical review of one defined AI-supported healthcare workflow.
- Workflow review
- Clinical stress-testing
- Patient-safety findings
- Critical edge cases
- Prioritised written report
Clinical review grounded in real healthcare practice.
Clinical AI Check brings senior nursing experience into the evaluation of AI-supported healthcare workflows.
Reviews focus on patient-safety risk, clinical usability, workflow design, edge cases and the conditions required for responsible human use of AI.
The focus is the gap between technical performance and clinical reality: how AI-generated information is interpreted, prioritised and acted upon inside real healthcare workflows.
Led by Aneth Olofsson, RN · 27 years clinical experience
Find the clinical problems while they are still cheap to fix.
Send a short description of the product, intended clinical user and workflow. We can then determine whether a focused Clinical AI Readiness Review is appropriate.
Request a review