Clinical AI, Quality & Safety
Connect model performance to patient-safe operations
What this role is up against
Clinical AI governance cannot end with validation metrics.
A model may perform well in testing and still create risk when data shifts, workflow changes, users misunderstand its role, integrations fail, or the organization cannot determine how the system influenced a consequential decision.
Quality and safety leaders need a lifecycle view that connects clinical evidence, intended use, operating boundaries, monitoring, escalation, human authority, and post-event learning.
Where TTIC connects
TTIC helps integrate those requirements with engineering, security, research, governance, and executive operations.
What success looks like
The objective is to make AI quality observable in the real environment where care is delivered. That gives clinical leaders a better basis for deciding when to adopt, when to constrain, when to investigate, and when to stop a system before uncertainty becomes patient harm.
Read the full Chair's Letter
Building the Institutional Infrastructure for Trustworthy AI | the complete letter from TTIC's Founder & Chair.
Standards and Adoption | evidence of where TTIC's accredited standards are cited and used.
Origin Story | where TTIC came from and why it was built this way.
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- Published by
- Trustworthy Technology & Innovation Consortium (TTIC)
- Author
- By Sherri Douville, Founder & Chair, Trustworthy Technology & Innovation Consortium (TTIC)
- Originally published
- Last updated
Cite this resource
Sherri Douville. “Clinical AI, Quality & Safety.” Trustworthy Technology & Innovation Consortium (TTIC), 2026. https://trustworthytechnologyinnovation.com/chairs-letter/clinical-ai-quality-safety/.