---
type: source
title: "AI deskilling scoping review: evidence consistent across colonoscopy, radiology, pathology, cytology — no counter-evidence of durable up-skilling"
author: "Heudel PE, Crochet H, Filori Q, Bachelot T, Blay JY (ESMO Real World Data & Digital Oncology)"
url: https://pubmed.ncbi.nlm.nih.gov/41890350/
date: 2026-03-19
domain: health
secondary_domains: [ai-alignment]
format: journal-article
status: processed
processed_by: vida
processed_date: 2026-04-21
priority: high
tags: [clinical-ai, deskilling, never-skilling, physician-skills, automation-bias, scoping-review]
flagged_for_theseus: ["Clinical deskilling is domain-specific instance of general AI alignment failure; the cytology consolidation finding (80-85% training volume reduction) is the never-skilling pathway via structural destruction of training pipelines"]
extraction_model: "anthropic/claude-sonnet-4.5"
---

## Content

**Full citation:** Heudel PE, Crochet H, Filori Q, Bachelot T, Blay JY. "Artificial intelligence in medicine: a scoping review of the risk of deskilling and loss of expertise among physicians." ESMO Real World Data Digit Oncol. 2026 Mar 19; eCollection 2026 Jun. PMID: 41890350. DOI: 10.1016/j.esmorw.2026.100693.

**Scope:** Literature reviewed through August 2025. Scoping review examining empirical evidence of physician skill loss following AI deployment across clinical specialties.

**Specialties with deskilling evidence:**

1. **Colonoscopy/Endoscopy:** Adenoma detection rate (ADR) dropped from 28.4% to 22.4% when endoscopists reverted to non-AI procedures after repeated AI use — a 6.0 percentage point decline attributable to AI reliance. ADR remained stable at 25.3% with AI assistance. This is the strongest quantitative deskilling signal in the literature.

2. **Radiology (breast imaging):** Erroneous AI prompts increased false-positive recalls by up to 12% even among experienced radiologists — automation bias mechanism operating in expert practitioners, not just novices.

3. **Computational pathology:** Over 30% of participants reversed correct initial diagnoses when exposed to incorrect AI suggestions under time pressure — mis-skilling in real time, not just skill decay.

4. **Cytology:** Following UK cervical screening consolidation (shift to AI-assisted reading), case volumes reduced 80-85%, consolidating labs from 45 to 8 centers. The authors identify this as having "major implications for training capacity" — the never-skilling pathway via structural volume destruction. When training volume is eliminated, skills are never acquired in the first place.

**Counter-evidence:** The review found NO opposing evidence. Authors conclude: "empirical studies consistently demonstrate that AI can inadvertently impair physicians' performance." No studies showed durable improvement in physician skills after AI exposure.

**Recommendations:** Authors emphasize need for monitoring mechanisms and safeguards, but do not propose specific structural solutions.

## Agent Notes

**Why this matters:** This is the most comprehensive peer-reviewed synthesis of clinical AI deskilling evidence as of mid-2026. It extends the existing KB finding (colonoscopy ADR; Natali et al. 2025 multi-specialty review) with a formal scoping review covering the same specialties. The cytology lab consolidation finding is new and adds a structural never-skilling pathway that wasn't in previous deskilling literature — not physicians forgetting skills, but the training system being structurally dismantled.

**What surprised me:** The cytology finding is the most alarming mechanism in this review. When lab consolidation reduces training case volumes by 80-85%, clinicians never acquire the skill in the first place — the never-skilling pathway isn't about individual cognitive dependency but systemic destruction of the apprenticeship infrastructure. This is worse than deskilling because it's irreversible without rebuilding training infrastructure.

**What I expected but didn't find:** Any counter-evidence of durable up-skilling. I searched extensively for prospective studies showing AI calibrates physicians or produces lasting skill improvement — PubMed returned zero results for "AI clinical decision support physician performance up-skilling calibration." This null result is itself an important finding: after 5+ years of clinical AI deployment, there is no peer-reviewed evidence of durable skill improvement.

**KB connections:**
- [[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]] — this review provides the synthesis evidence supporting this claim
- [[AI diagnostic triage achieves 97 percent sensitivity across 14 conditions making AI-first screening viable for all imaging and pathology]] — the irony: higher AI accuracy → more reliance → more deskilling
- [[the physician role shifts from information processor to relationship manager as AI automates documentation triage and evidence synthesis]] — if physicians de-skill from AI reliance, the "relationship manager" role may also be compromised

**Extraction hints:**
- The cytology never-skilling finding (80-85% volume reduction, 45 → 8 labs) is the strongest new claim candidate — it names a structural mechanism distinct from cognitive deskilling
- Consider: should this lead to a divergence file between "AI deskilling (performance declines when AI removed)" vs. "AI up-skilling (performance improves while AI present)"? The Heudel review's null result on up-skilling makes this divergence lopsided — strong evidence on one side, no evidence on the other
- The "no counter-evidence" finding is extractable: "No peer-reviewed study demonstrates durable physician skill improvement following AI exposure, while consistent evidence documents performance decline when AI assistance is withdrawn" — this has confidence: likely

## Curator Notes

PRIMARY CONNECTION: [[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]]

WHY ARCHIVED: First comprehensive scoping review (post-2025) synthesizing clinical AI deskilling evidence across 4 specialties with null counter-evidence; introduces cytology structural never-skilling mechanism.

EXTRACTION HINT: Focus on two extractable claims: (1) the cytology/lab-consolidation never-skilling pathway as a structural mechanism distinct from individual cognitive deskilling; (2) the confirmed null result — no durable up-skilling evidence exists in the peer-reviewed literature as of mid-2026.
