vida: extract claims from 2026-04-22-pmc11919318-pathology-ai-era-deskilling

- Source: inbox/queue/2026-04-22-pmc11919318-pathology-ai-era-deskilling.md
- Domain: health
- Claims: 2, Entities: 0
- Enrichments: 3
- Extracted by: pipeline ingest (OpenRouter anthropic/claude-sonnet-4.5)

Pentagon-Agent: Vida <PIPELINE>

domains/health/ai-case-routing-prevents-threshold-calibration-skill-development.md

@@ -0,0 +1,18 @@
+---
+type: claim
+domain: health
+description: When AI systems determine which cases humans review, trainees never learn the meta-skill of calibrating what constitutes routine versus flagged cases
+confidence: experimental
+source: Academic Pathology Journal (PMC11919318), pathology training commentary
+created: 2026-04-22
+title: AI-defined case routing prevents trainees from developing threshold-setting skills required for independent practice
+agent: vida
+sourced_from: health/2026-04-22-pmc11919318-pathology-ai-era-deskilling.md
+scope: functional
+sourcer: Academic Pathology Journal
+related: ["ai-cervical-cytology-screening-creates-never-skilling-through-routine-case-routing", "automation-bias-in-medicine-increases-false-positives-through-anchoring-on-ai-output", "clinical-ai-creates-three-distinct-skill-failure-modes-deskilling-misskilling-neverskilling"]
+---
+
+# AI-defined case routing prevents trainees from developing threshold-setting skills required for independent practice
+
+The paper identifies a novel meta-skill concern beyond diagnostic skill loss: the ability to calibrate thresholds for case prioritization is itself a skill that AI automation may prevent from developing. The authors note that 'only human experts can revise the thresholds for case prioritization,' but this assumes experts exist who developed threshold-setting skills before AI deployment. For trainees who enter training after AI integration, the AI defines what cases they encounter — they never develop independent judgment about what should be flagged versus routine. This is distinct from pattern recognition skills because it's about the higher-order judgment of what patterns warrant attention. The paper frames this as particularly concerning in pathology where 'AI may perform well in aggregate but fail on rare variants' — exactly the cases where independent threshold judgment is most critical. Proposed mitigation is 'graduated autonomy' where baseline competence is demonstrated before AI assistance increases, but this requires knowing what baseline competence looks like, which becomes unclear when AI is integrated from the start of training.

domains/health/ai-cervical-cytology-screening-creates-never-skilling-through-routine-case-routing.md

@@ -0,0 +1,19 @@
+---
+type: claim
+domain: health
+description: AI automation of routine cervical screening cases prevents trainees from developing the diagnostic acumen necessary for independent practice because routine cases are where foundational pattern recognition develops
+confidence: experimental
+source: Academic Pathology Journal (PMC11919318), commentary by pathology training experts
+created: 2026-04-22
+title: AI-integrated cervical cytology screening reduces trainee exposure to routine cases creating never-skilling risk for foundational pattern recognition
+agent: vida
+sourced_from: health/2026-04-22-pmc11919318-pathology-ai-era-deskilling.md
+scope: structural
+sourcer: Academic Pathology Journal
+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: ["clinical-ai-creates-three-distinct-skill-failure-modes-deskilling-misskilling-neverskilling", "never-skilling-is-detection-resistant-and-unrecoverable-making-it-worse-than-deskilling", "cytology-lab-consolidation-creates-never-skilling-pathway-through-80-percent-training-volume-destruction"]
+---
+
+# AI-integrated cervical cytology screening reduces trainee exposure to routine cases creating never-skilling risk for foundational pattern recognition
+
+The paper identifies cervical cytology screening as a primary automation target where AI handles large volumes of routine cases. This creates a structural never-skilling mechanism: (1) AI automation reduces trainee direct engagement with case diversity, (2) routine cases are precisely where foundational pattern recognition develops, (3) reduced exposure prevents development of 'diagnostic acumen necessary for independent practice,' (4) the skill deficit won't manifest until trainees become independent practitioners facing edge cases without foundational grounding. The paper notes that 'only human experts can revise the thresholds for case prioritization' — meaning AI defines what humans see, and trainees trained under AI threshold systems may never learn to set thresholds themselves. This is distinct from deskilling (loss of existing skills) because trainees never acquire the baseline competence in the first place. The mechanism is particularly insidious because it's detection-resistant: there's no pre-AI baseline to compare against for individual trainees who enter training after AI deployment.

domains/health/clinical-ai-creates-three-distinct-skill-failure-modes-deskilling-misskilling-neverskilling.md

@@ -66,3 +66,10 @@ UK cytology lab consolidation provides first structural never-skilling mechanism
 **Source:** PubMed systematic search, April 21, 2026
 
 The complete absence of peer-reviewed evidence for durable up-skilling after 5+ years of large-scale clinical AI deployment provides negative confirmation that skill effects flow in one direction. Despite extensive evidence on AI improving performance while present, zero published studies demonstrate improvement that persists when AI is removed. This asymmetry—growing deskilling literature (Heudel et al. 2026, Natali et al. 2025, colonoscopy ADR drop, radiology/pathology automation bias) versus empty up-skilling literature—confirms the three failure modes operate without a compensating improvement mechanism.
+
+
+## Supporting Evidence
+
+**Source:** Academic Pathology Journal (PMC11919318), 2025
+
+Academic Pathology Journal commentary provides pathology-specific confirmation of never-skilling mechanism through cervical cytology screening automation. Paper documents that AI automation of 'routine processes, such as initial screenings and pattern recognition in straightforward cases' reduces trainee exposure to the full spectrum of findings, and routine cases are precisely where foundational pattern recognition develops. Proposes 'graduated autonomy' model as mitigation but acknowledges difficulty of establishing baseline competence when AI is integrated from start of training.

domains/health/cytology-lab-consolidation-creates-never-skilling-pathway-through-80-percent-training-volume-destruction.md

@@ -10,9 +10,16 @@ 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"]
+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"]
 ---
 
 # 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.
+
+
+## Extending Evidence
+
+**Source:** Academic Pathology Journal (PMC11919318), 2025
+
+While this paper does not provide the 80-85% training volume reduction figure, it confirms the structural mechanism: AI automation of routine cervical cytology screening reduces trainee case exposure, and routine cases are where foundational skills develop. Paper identifies cervical cytology as a 'primary automation target' where AI handles 'large volumes of routine cases,' creating the pathway for volume destruction even without quantifying the exact reduction.

domains/health/never-skilling-is-detection-resistant-and-unrecoverable-making-it-worse-than-deskilling.md

@@ -1,27 +1,17 @@
 ---
-agent: vida
-confidence: experimental
-created: 2026-04-13
-description: Unlike deskilling (loss of previously acquired skills), never-skilling prevents initial skill formation and is undetectable because neither trainee nor supervisor can identify what was never
-  developed
-domain: health
-related:
-- AI-induced deskilling follows a consistent cross-specialty pattern where AI assistance improves performance while present but creates cognitive dependency that degrades performance when AI is unavailable
-- 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
-- clinical-ai-creates-three-distinct-skill-failure-modes-deskilling-misskilling-neverskilling
-- ai-assistance-produces-neurologically-grounded-irreversible-deskilling-through-prefrontal-disengagement-hippocampal-reduction-and-dopaminergic-reinforcement
-- delegating critical infrastructure development to AI creates civilizational fragility because humans lose the ability to understand maintain and fix the systems civilization depends on
-related_claims:
-- '[[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]]'
-reweave_edges:
-- AI-induced deskilling follows a consistent cross-specialty pattern where AI assistance improves performance while present but creates cognitive dependency that degrades performance when AI is unavailable|related|2026-04-14
-scope: causal
-source: Journal of Experimental Orthopaedics (March 2026), NEJM (2025-2026), Lancet Digital Health (2025)
-sourcer: Journal of Experimental Orthopaedics / Wiley
-title: Never-skilling — the failure to acquire foundational clinical competencies because AI was present during training — poses a detection-resistant, potentially unrecoverable threat to medical education
-  that is structurally worse than deskilling
 type: claim
+domain: health
+description: Unlike deskilling (loss of previously acquired skills), never-skilling prevents initial skill formation and is undetectable because neither trainee nor supervisor can identify what was never developed
+confidence: experimental
+source: Journal of Experimental Orthopaedics (March 2026), NEJM (2025-2026), Lancet Digital Health (2025)
+created: 2026-04-13
+agent: vida
+related: ["AI-induced deskilling follows a consistent cross-specialty pattern where AI assistance improves performance while present but creates cognitive dependency that degrades performance when AI is unavailable", "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", "clinical-ai-creates-three-distinct-skill-failure-modes-deskilling-misskilling-neverskilling", "ai-assistance-produces-neurologically-grounded-irreversible-deskilling-through-prefrontal-disengagement-hippocampal-reduction-and-dopaminergic-reinforcement", "delegating critical infrastructure development to AI creates civilizational fragility because humans lose the ability to understand maintain and fix the systems civilization depends on", "cytology-lab-consolidation-creates-never-skilling-pathway-through-80-percent-training-volume-destruction"]
+related_claims: ["[[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]]"]
+reweave_edges: ["AI-induced deskilling follows a consistent cross-specialty pattern where AI assistance improves performance while present but creates cognitive dependency that degrades performance when AI is unavailable|related|2026-04-14"]
+scope: causal
+sourcer: Journal of Experimental Orthopaedics / Wiley
+title: Never-skilling — the failure to acquire foundational clinical competencies because AI was present during training — poses a detection-resistant, potentially unrecoverable threat to medical education that is structurally worse than deskilling
 ---
 
 # Never-skilling — the failure to acquire foundational clinical competencies because AI was present during training — poses a detection-resistant, potentially unrecoverable threat to medical education that is structurally worse than deskilling
@@ -33,3 +23,10 @@ Never-skilling is formally defined in peer-reviewed literature as distinct from
 **Source:** Heudel PE et al. 2026
 
 Cytology lab consolidation demonstrates unrecoverability: 37 labs closed (45 to 8), 80-85% training volume eliminated. Reversing this requires rebuilding physical infrastructure, not just retraining individuals. This confirms never-skilling is structurally worse than deskilling because the recovery path requires institutional reconstruction.
+
+
+## Supporting Evidence
+
+**Source:** Academic Pathology Journal (PMC11919318), 2025
+
+Pathology training commentary confirms detection-resistance through lack of pre-AI baseline: trainees who enter training after AI deployment have no individual performance history to compare against. Paper notes that 'the skill deficit won't manifest until trainees become independent practitioners facing edge cases' — creating a delayed detection problem where incompetence only becomes visible after credentialing is complete.