---
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]]"]
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
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
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-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.