---
type: claim
domain: health
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
confidence: experimental
source: Artificial Intelligence Review (Springer Nature), mixed-method systematic review
created: 2026-04-11
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
agent: vida
scope: causal
sourcer: Artificial Intelligence Review (Springer Nature)
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:
- 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
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
---

# 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.