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 +1,19 @@
+---
+type: claim
+domain: health
+description: When AI determines which cases humans review, trainees never learn to calibrate 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: structural
+sourcer: Academic Pathology Journal
+supports: ["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"]
+---
+
+# AI-defined case routing prevents trainees from developing threshold-setting skills required for independent practice
+
+The paper notes that 'only human experts can revise the thresholds for case prioritization'—but this statement reveals a deeper problem: AI defines what humans see in the first place. When trainees are trained under an AI threshold system, they encounter only the cases the AI routes to them. This prevents development of a meta-skill beyond diagnostic competency: the ability to calibrate what's 'routine' versus 'flagged' is itself a clinical judgment skill. Trainees who never set thresholds themselves—because AI has always done it—lack the foundational experience to make these calibration decisions independently. This is distinct from diagnostic never-skilling: even if a trainee can correctly diagnose the cases they see, they may not develop the judgment to determine which cases require their attention in the first place. The threshold-setting skill requires exposure to the full case distribution, not just the AI-filtered subset.

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

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+---
+type: claim
+domain: health
+description: Automation of routine cervical screening cases prevents trainees from developing the baseline diagnostic acumen required for independent practice
+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 skills
+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-distinct-from-deskilling-affects-trainees-not-experienced-physicians"]
+related: ["cytology-lab-consolidation-creates-never-skilling-pathway-through-80-percent-training-volume-destruction", "clinical-ai-creates-three-distinct-skill-failure-modes-deskilling-misskilling-neverskilling", "never-skilling-distinct-from-deskilling-affects-trainees-not-experienced-physicians"]
+---
+
+# AI-integrated cervical cytology screening reduces trainee exposure to routine cases creating never-skilling risk for foundational pattern recognition skills
+
+AI automation in cervical cytology screening targets 'routine processes, such as initial screenings and pattern recognition in straightforward cases' for efficiency gains. However, these routine cases are precisely where trainees develop foundational pattern recognition skills. As AI handles large volumes of routine cervical screens, trainees see fewer cases across the full spectrum of findings. The paper notes this creates a risk where reduced case exposure prevents development of 'diagnostic acumen necessary for independent practice.' This is a structural never-skilling mechanism: the skill deficit won't manifest until trainees become independent practitioners facing edge cases without foundational grounding. The concern is particularly acute because AI may perform well in aggregate but fail on rare variants—exactly the cases humans need exposure to during training to handle them later. Unlike deskilling (where experienced practitioners lose existing skills), never-skilling affects trainees who never acquire the baseline competency in the first place.

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

@@ -80,3 +80,10 @@ Oettl et al. 2026 explicitly distinguishes never-skilling from deskilling, notin
 **Source:** Oettl et al. 2026
 
 Oettl et al. explicitly distinguish never-skilling (trainees never developing foundational competencies) from deskilling (experienced physicians losing existing skills), noting that 'educators may lack expertise supervising AI use' which compounds the never-skilling risk. This adds population-specific mechanism detail to the three-mode framework.
+
+
+## Supporting Evidence
+
+**Source:** PMC11919318, Academic Pathology 2025
+
+Academic Pathology Journal commentary provides pathology-specific confirmation of never-skilling mechanism, noting that AI automation of routine cervical cytology screening reduces trainee exposure to foundational cases, preventing development of 'diagnostic acumen necessary for independent practice.' The paper explicitly distinguishes this from deskilling of experienced practitioners.

domains/health/never-skilling-distinct-from-deskilling-affects-trainees-not-experienced-physicians.md

@@ -16,3 +16,10 @@ related: ["cytology-lab-consolidation-creates-never-skilling-pathway-through-80-
 # Never-skilling is mechanistically distinct from deskilling because it affects trainees who lack baseline competency rather than experienced physicians losing existing skills
 
 Oettl et al. explicitly distinguish 'never-skilling' from deskilling as separate mechanisms with different populations and dynamics. Deskilling affects experienced physicians who have baseline competency and lose it through AI reliance. Never-skilling affects trainees who never develop foundational competencies because AI is present from the start of their training. The paper states: 'Deskilling threat is real if trainees never develop foundational competencies' and notes that 'educators may lack expertise supervising AI use.' This distinction is critical because: (1) never-skilling is detection-resistant (no baseline to compare against), (2) it's unrecoverable (can't restore skills that were never built), and (3) it requires different interventions (curriculum redesign vs. retraining). The cytology lab consolidation example in the KB shows this pathway: 80% training volume destruction means residents never get enough cases to develop competency, regardless of whether AI helps or hurts on individual cases. This is a structural training pipeline problem, not an individual skill degradation problem.
+
+
+## Supporting Evidence
+
+**Source:** PMC11919318, Academic Pathology 2025
+
+Pathology training experts confirm the trainee-specific nature of never-skilling in cervical cytology: as AI handles routine screening cases, trainees see fewer cases across the full diagnostic spectrum, preventing baseline competency development. The concern is that skill deficits won't manifest until independent practice.

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

@@ -30,3 +30,10 @@ Cytology lab consolidation demonstrates unrecoverability: 37 labs closed (45 to
 **Source:** Oettl et al., Journal of Experimental Orthopaedics 2026
 
 Oettl et al. explicitly acknowledge that never-skilling is a genuine threat if 'trainees never develop foundational competencies' and note that 'educators may lack expertise supervising AI use,' compounding the detection problem. This supports the claim that never-skilling is structurally harder to address than deskilling.
+
+
+## Extending Evidence
+
+**Source:** PMC11919318, Academic Pathology 2025
+
+The threshold calibration skill deficit adds a detection-resistance mechanism: trainees may appear competent on the cases they see (AI-routed subset) but lack the judgment to determine which cases require attention in the first place. This meta-skill deficit only becomes visible when trainees must independently triage cases without AI routing.