---
agent: vida
confidence: experimental
created: 2026-04-11
description: Systematic taxonomy of AI-induced cognitive failures in medical practice, with never-skilling as a categorically different problem from deskilling because it lacks a baseline for comparison
domain: health
related:
- '{''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''}'
- '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'
- clinical-ai-creates-three-distinct-skill-failure-modes-deskilling-misskilling-neverskilling
- never-skilling-is-detection-resistant-and-unrecoverable-making-it-worse-than-deskilling
- ai-induced-deskilling-follows-consistent-cross-specialty-pattern-in-medicine
- never-skilling-is-structurally-invisible-because-it-lacks-pre-ai-baseline-requiring-prospective-competency-assessment
- ai-assistance-produces-neurologically-grounded-irreversible-deskilling-through-prefrontal-disengagement-hippocampal-reduction-and-dopaminergic-reinforcement
- 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]]'
- '[[divergence-human-ai-clinical-collaboration-enhance-or-degrade]]'
reweave_edges:
- Never-skilling in clinical AI is structurally invisible because it lacks a pre-AI baseline for comparison, requiring prospective competency assessment before AI exposure to detect|supports|2026-04-12
- '{''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''}'
- 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|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|supports|2026-04-14
- 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|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|related|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|related|2026-04-19'
scope: causal
source: Artificial Intelligence Review (Springer Nature), mixed-method systematic review
sourced_from:
- inbox/archive/health/2026-04-13-natali-2025-ai-deskilling-comprehensive-review.md
sourcer: Artificial Intelligence Review (Springer Nature)
supports:
- Never-skilling in clinical AI is structurally invisible because it lacks a pre-AI baseline for comparison, requiring prospective competency assessment before AI exposure to detect
- '{''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''}'
- 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
- 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
- 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
title: Clinical AI introduces three distinct skill failure modes — deskilling (existing expertise lost through disuse), mis-skilling (AI errors adopted as correct), and never-skilling (foundational competence
  never acquired) — requiring distinct mitigation strategies for each
type: claim
---

# Clinical AI introduces three distinct skill failure modes — deskilling (existing expertise lost through disuse), mis-skilling (AI errors adopted as correct), and never-skilling (foundational competence never acquired) — requiring distinct mitigation strategies for each

This systematic review identifies three mechanistically distinct pathways through which clinical AI degrades physician competence. **Deskilling** occurs when existing expertise atrophies through disuse: colonoscopy polyp detection dropped from 28.4% to 22.4% after 3 months of AI use, and experienced radiologists showed 12% increased false-positive recalls after exposure to erroneous AI prompts. **Mis-skilling** occurs when clinicians actively learn incorrect patterns from systematically biased AI outputs: in computational pathology studies, 30%+ of participants reversed correct initial diagnoses after exposure to incorrect AI suggestions under time constraints. **Never-skilling** is categorically different: trainees who begin clinical education with AI assistance may never develop foundational competencies. Junior radiologists are far less likely than senior colleagues to detect AI errors — not because they've lost skills, but because they never acquired them. This is structurally invisible because there's no pre-AI baseline to compare against. The review documents mitigation strategies including AI-off drills, structured assessment pre-AI review, and curriculum redesign with explicit competency development before AI exposure. The key insight is that these three failure modes require fundamentally different interventions: deskilling requires practice maintenance, mis-skilling requires error detection training, and never-skilling requires prospective competency assessment before AI exposure.

## Extending Evidence

**Source:** Heudel PE et al. 2026, UK cervical screening consolidation

UK cytology lab consolidation provides first structural never-skilling mechanism: 80-85% training volume reduction through consolidation from 45 to 8 labs. This extends the never-skilling concept from individual cognitive failure to institutional infrastructure destruction. The mechanism is not 'physicians never learn because AI does it for them' but 'training infrastructure is dismantled so learning becomes impossible.'


## Supporting Evidence

**Source:** PubMed systematic search, April 21, 2026

The complete absence of peer-reviewed evidence for durable up-skilling after 5+ years of large-scale clinical AI deployment provides negative confirmation that skill effects flow in one direction. Despite extensive evidence on AI improving performance while present, zero published studies demonstrate improvement that persists when AI is removed. This asymmetry—growing deskilling literature (Heudel et al. 2026, Natali et al. 2025, colonoscopy ADR drop, radiology/pathology automation bias) versus empty up-skilling literature—confirms the three failure modes operate without a compensating improvement mechanism.
