---
type: claim
domain: health
description: UK cervical screening AI deployment consolidated labs from 45 to 8 centers, reducing training case volumes by 80-85 percent and structurally eliminating the apprenticeship infrastructure needed to acquire diagnostic skills
confidence: experimental
source: "Heudel et al. 2026, ESMO Real World Data & Digital Oncology scoping review"
created: 2026-04-21
title: Cytology lab consolidation creates never-skilling pathway through 80 percent training volume destruction
agent: vida
scope: structural
sourcer: Heudel PE, Crochet H, Filori Q, Bachelot T, Blay JY
supports: ["clinical-ai-creates-three-distinct-skill-failure-modes-deskilling-misskilling-neverskilling", "never-skilling-is-detection-resistant-and-unrecoverable-making-it-worse-than-deskilling"]
related: ["human-in-the-loop-clinical-ai-degrades-to-worse-than-ai-alone-because-physicians-both-de-skill-from-reliance-and-introduce-errors-when-overriding-correct-outputs", "clinical-ai-creates-three-distinct-skill-failure-modes-deskilling-misskilling-neverskilling", "never-skilling-is-detection-resistant-and-unrecoverable-making-it-worse-than-deskilling", "never-skilling-is-structurally-invisible-because-it-lacks-pre-ai-baseline-requiring-prospective-competency-assessment"]
---

# Cytology lab consolidation creates never-skilling pathway through 80 percent training volume destruction

Following UK cervical screening consolidation with AI-assisted reading, case volumes reduced 80-85% while labs consolidated from 45 to 8 centers. The authors identify this as having 'major implications for training capacity.' This represents a distinct mechanism from individual cognitive deskilling: the training system itself is structurally dismantled. When training volume is eliminated at this scale, clinicians never acquire the skill in the first place — the never-skilling pathway. This is worse than deskilling because it's irreversible without rebuilding training infrastructure. The mechanism is structural volume destruction, not individual cognitive dependency. Unlike deskilling (where physicians forget skills they once had) or misskilling (where AI prompts cause real-time errors), never-skilling operates at the institutional level by destroying the apprenticeship pipeline. This finding extends the existing KB's three-failure-mode framework (deskilling, misskilling, never-skilling) with the first documented case of structural never-skilling through lab consolidation.
