Solutions
Make healthcare AI controls part of the workflow
We help teams turn responsible-AI principles into concrete product and operating decisions that users can follow and reviewers can inspect.
Governance is strongest when it is visible in the system: what data enters, what the AI may do, who reviews it, what gets logged and what happens when confidence is low.
Try the on-device de-identification demoSee common identifiers removed from clinical text in your browser, without uploading the pasted text.Problems we address
- The intended use and system boundaries are vague
- Sensitive data is used without minimisation
- Human review exists only as a policy statement
- Edge cases and failures are not tested
- No acceptance threshold has been agreed
- Logs, monitoring and incident ownership are unclear
What we do
Services
- Intended-use and boundary definition
- Workflow-level risk assessment
- Data minimisation and de-identification design
- Human-review and escalation design
- Evaluation scenarios and acceptance criteria
- Failure and fallback planning
- Audit-trail requirements
- Monitoring and review plans
Responsible use
Controls must be proportionate to intended use and risk. High-risk healthcare decisions require appropriate human oversight, validation and accountable governance.
Scope of this service
This service supports product and workflow governance. It is not legal advice, regulatory certification, a clinical-safety sign-off or an information-security audit.
For non-healthcare business automation, visit AIOS by Ayothedoc.