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, creating a meta-skill deficit beyond diagnostic accuracy
+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-structurally-invisible-because-it-lacks-pre-ai-baseline-requiring-prospective-competency-assessment"]
+related: ["clinical-ai-creates-three-distinct-skill-failure-modes-deskilling-misskilling-neverskilling", "never-skilling-is-structurally-invisible-because-it-lacks-pre-ai-baseline-requiring-prospective-competency-assessment"]
+---
+
+# AI-defined case routing prevents trainees from developing threshold-setting skills required for independent practice
+
+The paper identifies a novel meta-skill concern: 'only human experts can revise the thresholds for case prioritization' in AI-integrated workflows. This reveals that AI doesn't just automate diagnostic tasks—it defines the scope of human review by setting routing thresholds. Trainees working under AI-defined routing systems may never develop the ability to calibrate thresholds themselves. This is distinct from diagnostic skill: it's the judgment of what constitutes a routine case versus one requiring escalation. The paper argues this threshold-setting ability is itself a clinical skill that requires exposure to the full case distribution. When AI pre-filters cases, trainees only see what the AI flags, preventing them from developing independent judgment about case prioritization. This creates a dependency where practitioners can only work within AI-defined parameters because they never learned to set parameters independently. The concern is that this meta-skill deficit won't manifest until trainees become independent practitioners and must make threshold decisions without AI scaffolding.

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 by reducing exposure to the full spectrum of findings
+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: ["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", "never-skilling-is-detection-resistant-and-unrecoverable-making-it-worse-than-deskilling", "ai-induced-deskilling-follows-consistent-cross-specialty-pattern-in-medicine"]
+---
+
+# AI-integrated cervical cytology screening reduces trainee exposure to routine cases creating never-skilling risk for foundational pattern recognition
+
+AI automation targets routine cervical cytology screening and histopathology cases specifically because these represent high-volume, pattern-recognition tasks. However, routine cases are precisely where foundational diagnostic skills develop in pathology training. The paper identifies a structural mechanism: as AI handles routine screenings, trainees see fewer cases across the full spectrum of findings. This creates a never-skilling pathway where trainees never develop the 'diagnostic acumen necessary for independent practice' because they lack exposure to the case diversity required for pattern recognition competence. The concern is particularly acute in cervical cytology, where AI can process large volumes but trainees need exposure to both normal and abnormal findings to calibrate their diagnostic thresholds. Unlike deskilling (where existing skills atrophy), never-skilling affects trainees who never acquire foundational competence in the first place. The paper notes this is a structural training problem, not a technology performance problem—AI may perform well in aggregate while simultaneously preventing skill development in the next generation of practitioners.

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.
+
+
+## Extending Evidence
+
+**Source:** Academic Pathology Journal (PMC11919318)
+
+Pathology training commentary identifies cervical cytology screening as a concrete example where AI automation of routine cases creates never-skilling pathway by reducing trainee exposure to the full spectrum of findings required for pattern recognition competence. The paper emphasizes that routine cases are precisely where foundational diagnostic skills develop, creating a structural contradiction where AI targets the same cases trainees need for skill acquisition.

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:** Academic Pathology Journal (PMC11919318)
+
+Pathology training experts explicitly distinguish never-skilling (trainees who never develop diagnostic acumen) from deskilling (experienced practitioners losing skills). The paper focuses specifically on training pathways and how AI integration affects skill acquisition during residency, not skill maintenance in practicing pathologists.

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)
+
+The paper notes that reduced case exposure prevents development of 'diagnostic acumen necessary for independent practice,' implying the skill deficit won't manifest until trainees become independent practitioners. This confirms the detection-resistance property—the problem is invisible during training when AI scaffolding is present and only becomes apparent when practitioners must work independently.