vida: extract claims from 2026-04-13-natali-2025-ai-deskilling-comprehensive-review

- Source: inbox/queue/2026-04-13-natali-2025-ai-deskilling-comprehensive-review.md
- Domain: health
- Claims: 2, Entities: 0
- Enrichments: 1
- 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

@@ -0,0 +1,17 @@
+---
+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
+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
+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]]"]
+---
+
+# 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
+
+Natali et al.'s systematic review across 10 medical specialties reveals a universal three-phase pattern: (1) AI assistance improves performance metrics while present, (2) extended AI use reduces opportunities for independent skill-building, and (3) performance degrades when AI becomes unavailable, demonstrating dependency rather than augmentation. Quantitative evidence includes: colonoscopy ADR dropping from 28.4% to 22.4% when endoscopists reverted to non-AI procedures after extended AI use (RCT); 30%+ of pathologists reversing correct initial diagnoses when exposed to incorrect AI suggestions under time pressure; 45.5% of ACL diagnosis errors resulting directly from following incorrect AI recommendations across all experience levels. The pattern's consistency across specialties as diverse as neurosurgery, anesthesiology, and geriatrics—not just image-reading specialties—suggests this is a fundamental property of how human cognitive architecture responds to reliable performance assistance, not a specialty-specific implementation problem. The proposed mechanism: AI assistance creates cognitive offloading where clinicians stop engaging prefrontal cortex analytical processes, hippocampal memory formation decreases over repeated exposure, and dopaminergic reinforcement of AI-reliance strengthens, producing skill degradation that becomes visible when AI is removed.

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

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+---
+type: claim
+domain: health
+description: Controlled study of 27 radiologists in mammography shows erroneous AI prompts systematically bias interpretation toward false positives through cognitive anchoring mechanism
+confidence: likely
+source: Natali et al. 2025 review, citing controlled mammography study with 27 radiologists
+created: 2026-04-13
+title: 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
+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]]"]
+---
+
+# 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
+
+A controlled study of 27 radiologists performing mammography reads found that erroneous AI prompts increased false-positive recalls by up to 12 percentage points, with the effect persisting across experience levels. The mechanism is automation bias: radiologists anchor on AI output rather than conducting fully independent reads, even when they possess the expertise to identify the error. This differs from simple deskilling—it's real-time mis-skilling where the AI's presence actively degrades decision quality below what the clinician would achieve independently. The finding is particularly significant because it occurs in experienced readers, suggesting automation bias is not a training problem but a fundamental feature of human-AI interaction in high-stakes decision contexts. Similar patterns appeared in computational pathology (30%+ diagnosis reversals under time pressure) and ACL diagnosis (45.5% of errors from following incorrect AI recommendations), indicating the mechanism generalizes across imaging modalities and clinical contexts.

domains/health/glp1-access-follows-systematic-inversion-highest-burden-states-have-lowest-coverage-and-highest-income-relative-cost.md

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+---
+type: claim
+domain: health
+description: The healthcare system systematically denies access to the populations with the highest disease burden through the combination of state Medicaid policy and income distribution
+confidence: likely
+source: KFF + Health Management Academy, 2025-2026 Medicaid coverage and spending analysis
+created: 2026-04-13
+title: GLP-1 access follows systematic inversion where states with highest obesity prevalence have both lowest Medicaid coverage rates and highest income-relative out-of-pocket costs
+agent: vida
+scope: structural
+sourcer: KFF + Health Management Academy
+related_claims: ["[[GLP-1 receptor agonists are the largest therapeutic category launch in pharmaceutical history but their chronic use model makes the net cost impact inflationary through 2035]]", "[[medical care explains only 10-20 percent of health outcomes because behavioral social and genetic factors dominate as four independent methodologies confirm]]"]
+---
+
+# GLP-1 access follows systematic inversion where states with highest obesity prevalence have both lowest Medicaid coverage rates and highest income-relative out-of-pocket costs
+
+States with the highest obesity rates (Mississippi, West Virginia, Louisiana at 40%+ prevalence) face a triple barrier: (1) only 13 state Medicaid programs cover GLP-1s for obesity as of January 2026 (down from 16 in 2025), and high-burden states are least likely to be among them; (2) these states have the lowest per-capita income; (3) the combination creates income-relative costs of 12-13% of median annual income to maintain continuous GLP-1 treatment in Mississippi/West Virginia/Louisiana tier versus below 8% in Massachusetts/Connecticut tier. Meanwhile, commercial insurance (43% of plans include weight-loss coverage) concentrates in higher-income populations, creating 8x higher GLP-1 utilization in commercial versus Medicaid on a cost-per-prescription basis. This is not an access gap (implying a pathway to close it) but an access inversion—the infrastructure systematically works against the populations who would benefit most. Survey data confirms the structural reality: 70% of Americans believe GLP-1s are accessible only to wealthy people, and only 15% think they're available to anyone who needs them. The majority could afford $100/month or less while standard maintenance pricing is ~$350/month even with manufacturer discounts.

inbox/archive/health/2026-04-13-natali-2025-ai-deskilling-comprehensive-review.md

@@ -7,10 +7,13 @@ date: 2025-01-01
 domain: health
 secondary_domains: [ai-alignment]
 format: article
-status: unprocessed
+status: processed
+processed_by: vida
+processed_date: 2026-04-13
 priority: high
 tags: [clinical-ai, deskilling, automation-bias, medical-education, ai-safety, cross-specialty]
 flagged_for_theseus: ["Cross-specialty deskilling evidence body directly relevant to AI safety in high-stakes domains; neurological mechanism proposed; automation bias in medical context"]
+extraction_model: "anthropic/claude-sonnet-4.5"
 ---
 
 ## Content

inbox/null-result/2026-04-13-noom-glp1-engagement-report-persistence-2026.md

@@ -7,9 +7,10 @@ date: 2026-02-04
 domain: health
 secondary_domains: []
 format: report
-status: unprocessed
+status: null-result
 priority: medium
 tags: [glp1, adherence, behavioral-wraparound, digital-health, noom, engagement, persistence]
+extraction_model: "anthropic/claude-sonnet-4.5"
 ---
 
 ## Content