vida: extract claims from 2026-04-22-oettl-2026-ai-deskilling-to-upskilling-orthopedics

- Source: inbox/queue/2026-04-22-oettl-2026-ai-deskilling-to-upskilling-orthopedics.md
- Domain: health
- Claims: 0, Entities: 0
- Enrichments: 4
- Extracted by: pipeline ingest (OpenRouter anthropic/claude-sonnet-4.5)

Pentagon-Agent: Vida <PIPELINE>

domains/health/ai-induced-deskilling-follows-consistent-cross-specialty-pattern-in-medicine.md

@@ -1,44 +1,19 @@
 ---
-agent: vida
-confidence: likely
-created: 2026-04-13
-description: Systematic review across 10 medical specialties (radiology, neurosurgery, anesthesiology, oncology, cardiology, pathology, fertility medicine, geriatrics, psychiatry, ophthalmology) finds universal
-  pattern of skill degradation following AI removal
-domain: health
-related:
-- Automation bias in medical imaging causes clinicians to anchor on AI output rather than conducting independent reads, increasing false-positive rates by up to 12 percent even among experienced readers
-- ai-induced-deskilling-follows-consistent-cross-specialty-pattern-in-medicine
-- 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
-- never-skilling-is-structurally-invisible-because-it-lacks-pre-ai-baseline-requiring-prospective-competency-assessment
-- never-skilling-is-detection-resistant-and-unrecoverable-making-it-worse-than-deskilling
-- economic forces push humans out of every cognitive loop where output quality is independently verifiable because human-in-the-loop is a cost that competitive markets eliminate
-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 assistance may produce neurologically-grounded, partially irreversible skill degradation through three concurrent mechanisms'': ''prefrontal disengagement, hippocampal memory formation reduction,
-  and dopaminergic reinforcement of AI reliance|supports|2026-04-14''}'
-- Automation bias in medical imaging causes clinicians to anchor on AI output rather than conducting independent reads, increasing false-positive rates by up to 12 percent even among experienced readers|related|2026-04-14
-- Dopaminergic reinforcement of AI-assisted success creates motivational entrenchment that makes deskilling a behavioral incentive problem, not just a training design problem|supports|2026-04-14
-- '{''AI assistance may produce neurologically-grounded, partially irreversible skill degradation through three concurrent mechanisms'': ''prefrontal disengagement, hippocampal memory formation reduction,
-  and dopaminergic reinforcement of AI reliance|supports|2026-04-17''}'
-- '{''AI assistance may produce neurologically-grounded, partially irreversible skill degradation through three concurrent mechanisms'': ''prefrontal disengagement, hippocampal memory formation reduction,
-  and dopaminergic reinforcement of AI reliance|supports|2026-04-18''}'
-- 'AI assistance may produce neurologically-grounded, partially irreversible skill degradation through three concurrent mechanisms: prefrontal disengagement, hippocampal memory formation reduction, and
-  dopaminergic reinforcement of AI reliance|supports|2026-04-19'
-scope: causal
-source: Natali et al., Artificial Intelligence Review 2025, mixed-method systematic review
-sourced_from:
-- inbox/archive/health/2026-04-13-natali-2025-ai-deskilling-comprehensive-review.md
-sourcer: Natali et al.
-supports:
-- '{''AI assistance may produce neurologically-grounded, partially irreversible skill degradation through three concurrent mechanisms'': ''prefrontal disengagement, hippocampal memory formation reduction,
-  and dopaminergic reinforcement of AI reliance''}'
-- Dopaminergic reinforcement of AI-assisted success creates motivational entrenchment that makes deskilling a behavioral incentive problem, not just a training design problem
-- 'AI assistance may produce neurologically-grounded, partially irreversible skill degradation through three concurrent mechanisms: prefrontal disengagement, hippocampal memory formation reduction, and
-  dopaminergic reinforcement of AI reliance'
-title: 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
 type: claim
+domain: health
+description: Systematic review across 10 medical specialties (radiology, neurosurgery, anesthesiology, oncology, cardiology, pathology, fertility medicine, geriatrics, psychiatry, ophthalmology) finds universal pattern of skill degradation following AI removal
+confidence: likely
+source: Natali et al., Artificial Intelligence Review 2025, mixed-method systematic review
+created: 2026-04-13
+agent: vida
+related: ["Automation bias in medical imaging causes clinicians to anchor on AI output rather than conducting independent reads, increasing false-positive rates by up to 12 percent even among experienced readers", "ai-induced-deskilling-follows-consistent-cross-specialty-pattern-in-medicine", "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", "never-skilling-is-structurally-invisible-because-it-lacks-pre-ai-baseline-requiring-prospective-competency-assessment", "never-skilling-is-detection-resistant-and-unrecoverable-making-it-worse-than-deskilling", "economic forces push humans out of every cognitive loop where output quality is independently verifiable because human-in-the-loop is a cost that competitive markets eliminate", "no-peer-reviewed-evidence-of-durable-physician-upskilling-from-ai-exposure-as-of-mid-2026"]
+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 assistance may produce neurologically-grounded, partially irreversible skill degradation through three concurrent mechanisms': 'prefrontal disengagement, hippocampal memory formation reduction, and dopaminergic reinforcement of AI reliance|supports|2026-04-14'}", "Automation bias in medical imaging causes clinicians to anchor on AI output rather than conducting independent reads, increasing false-positive rates by up to 12 percent even among experienced readers|related|2026-04-14", "Dopaminergic reinforcement of AI-assisted success creates motivational entrenchment that makes deskilling a behavioral incentive problem, not just a training design problem|supports|2026-04-14", "{'AI assistance may produce neurologically-grounded, partially irreversible skill degradation through three concurrent mechanisms': 'prefrontal disengagement, hippocampal memory formation reduction, and dopaminergic reinforcement of AI reliance|supports|2026-04-17'}", "{'AI assistance may produce neurologically-grounded, partially irreversible skill degradation through three concurrent mechanisms': 'prefrontal disengagement, hippocampal memory formation reduction, and dopaminergic reinforcement of AI reliance|supports|2026-04-18'}", "AI assistance may produce neurologically-grounded, partially irreversible skill degradation through three concurrent mechanisms: prefrontal disengagement, hippocampal memory formation reduction, and dopaminergic reinforcement of AI reliance|supports|2026-04-19"]
+scope: causal
+sourced_from: ["inbox/archive/health/2026-04-13-natali-2025-ai-deskilling-comprehensive-review.md"]
+sourcer: Natali et al.
+supports: ["{'AI assistance may produce neurologically-grounded, partially irreversible skill degradation through three concurrent mechanisms': 'prefrontal disengagement, hippocampal memory formation reduction, and dopaminergic reinforcement of AI reliance'}", "Dopaminergic reinforcement of AI-assisted success creates motivational entrenchment that makes deskilling a behavioral incentive problem, not just a training design problem", "AI assistance may produce neurologically-grounded, partially irreversible skill degradation through three concurrent mechanisms: prefrontal disengagement, hippocampal memory formation reduction, and dopaminergic reinforcement of AI reliance"]
+title: 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
 ---
 
 # 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
@@ -50,3 +25,10 @@ Natali et al.'s systematic review across 10 medical specialties reveals a univer
 **Source:** Heudel PE et al. 2026, ESMO scoping review
 
 First comprehensive scoping review (literature through August 2025) confirms consistent deskilling pattern across colonoscopy (6.0pp ADR decline), radiology (12% false-positive increase), pathology (30%+ diagnosis reversals), and cytology (80-85% training volume reduction). Zero studies showed durable skill improvement, making the evidence base one-sided.
+
+
+## Challenging Evidence
+
+**Source:** Oettl et al., Journal of Experimental Orthopaedics 2026
+
+Oettl et al. present the strongest available counter-argument to medical AI deskilling, arguing that AI will 'necessitate an evolution of the physician's role' toward augmentation rather than replacement. They propose three upskilling mechanisms: micro-learning at point of care, liberation from administrative burden, and performance floor standardization. However, the paper is primarily theoretical—all empirical evidence cited measures concurrent AI-assisted performance rather than post-training skill retention.

domains/health/automation-bias-in-medicine-increases-false-positives-through-anchoring-on-ai-output.md

@@ -10,7 +10,7 @@ agent: vida
 scope: causal
 sourcer: Natali et al.
 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]]", "[[divergence-human-ai-clinical-collaboration-enhance-or-degrade]]"]
-related: ["automation-bias-in-medicine-increases-false-positives-through-anchoring-on-ai-output"]
+related: ["automation-bias-in-medicine-increases-false-positives-through-anchoring-on-ai-output", "optional-use-ai-deployment-preserves-independent-clinical-judgment-preventing-automation-bias-pathway"]
 ---
 
 # Automation bias in medical imaging causes clinicians to anchor on AI output rather than conducting independent reads, increasing false-positive rates by up to 12 percent even among experienced readers
@@ -23,3 +23,10 @@ A controlled study of 27 radiologists performing mammography reads found that er
 **Source:** Heudel PE et al. 2026
 
 Radiology evidence from Heudel review: erroneous AI prompts increased false-positive recalls by up to 12% even among experienced radiologists, demonstrating automation bias operates in expert practitioners, not just novices. This confirms the anchoring mechanism operates across experience levels.
+
+
+## Challenging Evidence
+
+**Source:** Oettl et al., Journal of Experimental Orthopaedics 2026
+
+Oettl et al. acknowledge automation bias exists but argue that requiring clinicians to 'review, confirm or override' AI recommendations creates a learning loop that mitigates bias. However, they provide no evidence that the review process prevents deference—only that performance improves when AI is present.

domains/health/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.md

@@ -5,7 +5,7 @@ description: Stanford-Harvard study shows AI alone 90 percent vs doctors plus AI
 confidence: likely
 source: DJ Patil interviewing Bob Wachter, Commonwealth Club, February 9 2026; Stanford/Harvard diagnostic accuracy study; European colonoscopy AI de-skilling study
 created: 2026-02-18
-related: ["economic forces push humans out of every cognitive loop where output quality is independently verifiable because human-in-the-loop is a cost that competitive markets eliminate", "divergence-human-ai-clinical-collaboration-enhance-or-degrade", "ai-induced-deskilling-follows-consistent-cross-specialty-pattern-in-medicine", "medical LLM benchmark performance does not translate to clinical impact because physicians with and without AI access achieve similar diagnostic accuracy in randomized trials", "no-peer-reviewed-evidence-of-durable-physician-upskilling-from-ai-exposure-as-of-mid-2026", "clinical-ai-creates-three-distinct-skill-failure-modes-deskilling-misskilling-neverskilling"]
+related: ["economic forces push humans out of every cognitive loop where output quality is independently verifiable because human-in-the-loop is a cost that competitive markets eliminate", "divergence-human-ai-clinical-collaboration-enhance-or-degrade", "ai-induced-deskilling-follows-consistent-cross-specialty-pattern-in-medicine", "medical LLM benchmark performance does not translate to clinical impact because physicians with and without AI access achieve similar diagnostic accuracy in randomized trials", "no-peer-reviewed-evidence-of-durable-physician-upskilling-from-ai-exposure-as-of-mid-2026", "clinical-ai-creates-three-distinct-skill-failure-modes-deskilling-misskilling-neverskilling", "automation-bias-in-medicine-increases-false-positives-through-anchoring-on-ai-output"]
 related_claims: ["ai-induced-deskilling-follows-consistent-cross-specialty-pattern-in-medicine", "never-skilling-is-detection-resistant-and-unrecoverable-making-it-worse-than-deskilling", "ai-assistance-produces-neurologically-grounded-irreversible-deskilling-through-prefrontal-disengagement-hippocampal-reduction-and-dopaminergic-reinforcement", "llms-amplify-human-cognitive-biases-through-sequential-processing-and-lack-contextual-resistance"]
 reweave_edges: ["NCT07328815 - Mitigating Automation Bias in Physician-LLM Diagnostic Reasoning|supports|2026-04-07", "Does human oversight improve or degrade AI clinical decision-making?|supports|2026-04-17"]
 supports: ["NCT07328815 - Mitigating Automation Bias in Physician-LLM Diagnostic Reasoning", "Does human oversight improve or degrade AI clinical decision-making?"]
@@ -82,3 +82,10 @@ Topics:
 **Source:** Oettl et al. 2026
 
 Oettl et al. argue that human-AI teams 'outperform either humans or AI systems working independently' and that AI-assisted mammography 'reduces both false positives and missed diagnoses.' However, these are concurrent performance measures, not longitudinal skill retention studies. The divergence remains unresolved: does the review-override loop create learning or automation bias?
+
+
+## Challenging Evidence
+
+**Source:** Oettl et al., Journal of Experimental Orthopaedics 2026
+
+Oettl et al. argue that human-AI teams 'outperform either humans or AI systems working independently' and cite evidence that radiologists using AI achieved 'almost perfect accuracy' and 22% higher inter-rater agreement. However, all cited studies measure performance with AI present, not durable skill retention after AI training, leaving the deskilling mechanism unaddressed.

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:** 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.