Clinical AI governance refers to the organizational, regulatory, and technical frameworks that assign accountability for AI-generated recommendations in clinical settings. GPe Research treats clinical AI governance as a practical discipline concerned with named ownership, procurement standards, post-market surveillance, and the design of interfaces that support deliberative rather than reflexive clinician judgment. Governance is distinct from transparency: knowing how a model works does not assign accountability for what it recommends.
A control that fires and a decision that is owned are two different events. Twenty-five years of clinical decision support has measured the first with precision and left the second unrecorded.
The framework that names who owns a decision when AI shapes it. Built in clinical AI, now operating in financial AI, and structurally the same in either room.
The nine blocks and the Gap Score™ methodology, generalized from their first application in clinical AI to operate in any room where an AI system produces an output and a named person has to answer for the decision.
The insurance market withdrew from generative AI on January 1. The institutions holding clinical AI risk, and the executives who approved it, are now holding it themselves.
A real handoff transfers a named owner along with the information. Clinical AI transfers the information and the liability, and leaves the sending seat empty.
A nine-block canvas and a three-layer Gap Score™ for surfacing, scoring, and closing the clinical AI accountability gap before a recommendation reaches a patient.
Every clinical AI deployment requires two named owners in the audit trail, not one: a Governance Owner and a Decision Owner.
