Development of a conceptual, AI-supported pre-screening and documentation approach for AI use cases as part of a study assignment at Karolinska Institutet (no implementation or regulatory approval).
Manual triage is slow, inconsistent and risky. Underclassification of critical cases may result in non-compliant applications and potential patient harm; Over-classification ties up scarce expert resources. RCI resolves this tension through uniform inputs, machine pre-evaluation and mandatory human review.
The core logic is risk-based: low-risk internal tools are passed through standardized controls more quickly; HCP or patient-related applications, medical intended use, personal data or complex vendor constellations trigger in-depth examination. This makes RCI a control instrument for innovation under compliance conditions.[3,4]
HITL design does not automatically prevent automation bias - uncritical acceptance of confident AI outputs is a documented patient safety risk.[15,16,21]
Primary Goal: HIGH-Class Recall — Misclassification as LOW/MEDIUM is more dangerous than overclassification.
The final compliance memo remains a professionally accountable human decision. The EU AI Act distinguishes between the roles and obligations of providers and operators or deployers; for RCI, this means that system boundaries, review decisions, escalations, source status and the audit trail must be documented. Specific liability questions must be assessed legally depending on the organization and the individual use case.[1,3,14,16]
Basis: Lecture notes “AI in Healthcare, Foundation and Technical Methods” (Abtahi & Astaraki, Karolinska Institutet) as well as the following peer-reviewed publications. Metadata and DOIs checked against primary sources (as of April 29, 2026).