VECTOR: A Quality Control Framework for Artificial Intelligence in the Medical Laboratory Decision Chain

Artificial intelligence is entering the clinical laboratory decision chain across an expanding spectrum of applications, from rule-based algorithms embedded in analyzers to commercial large language models used for interpretive commentary. Existing governance frameworks for healthcare AI address regulatory approval, ethical principles, and reporting standards, but none translate governance into the operational language that clinical laboratories have used for decades: multi-rule acceptance logic, constraint-based escalation, and periodic monitoring with defined intervention triggers. This paper introduces VECTOR (Verifiability, Evolution, Consequence, Task role, Oversight, Responsibility), a quality control framework for AI in the clinical laboratory decision chain. VECTOR is grounded in the design philosophy of statistical quality control: governance should be built on invariant questions tied to clinical consequences, not on AI architecture, vendor, or generation. Each system operating within the laboratory decision chain receives a six-dimensional risk-control profile, scored on an ordinal 1–5 scale. A composite score combined with five constraint rules—functioning analogously to critical laboratory values that override an otherwise normal panel—determines governance tier. Assessment is repeated periodically, producing a longitudinal governance record analogous to a Levey-Jennings chart. Stress testing across five scenarios (rule-based flagging in analyzers and LIS, a morphology classifier deployed in two workflow configurations, a commercial large language model used for laboratory result interpretation, semi-autonomous reflex test ordering, and longitudinal drift of a machine learning-based autoverification system) demonstrates that VECTOR is proportionate to risk, sensitive to deployment context rather than technology, actionable through targeted governance interventions, and capable of detecting governance drift that individual events would not reveal. VECTOR is offered as a conceptual foundation requiring empirical calibration through expert consensus and pilot implementation; it complements rather than replaces existing regulatory, ethical, and reporting frameworks, providing the operational layer that translates high-level governance principles into daily laboratory practice.