---
type: source
title: "From De-Skilling to Up-Skilling: Never-Skilling Named as Greater Long-Term Threat in Medical Education (JEO, March 2026)"
author: "Journal of Experimental Orthopaedics / Wiley (March 2026)"
url: https://esskajournals.onlinelibrary.wiley.com/doi/10.1002/jeo2.70677
date: 2026-03-01
domain: health
secondary_domains: [ai-alignment]
format: article
status: processed
processed_by: vida
processed_date: 2026-04-13
priority: medium
tags: [never-skilling, medical-education, clinical-ai, deskilling, ai-safety, orthopaedics]
flagged_for_theseus: ["Never-skilling named formally in peer-reviewed literature as distinct risk category from deskilling; provides language and framing for the AI capability → human deskilling pathway"]
extraction_model: "anthropic/claude-sonnet-4.5"
---

## Content

Journal of Experimental Orthopaedics (March 2026, Wiley): "From De-Skilling to Up-Skilling" — a review of AI's impact on clinical skill development, specifically naming never-skilling as a formal concern.

**Key passage (verbatim or close paraphrase):**
"Never-skilling poses a greater long-term threat to medical education than deskilling; it occurs when trainees rely on automation so early in their development that they fail to acquire foundational clinical reasoning and procedural competencies."

**Definition established:**
- *Deskilling:* Loss of skills previously acquired, due to reduced practice from AI assistance
- *Mis-skilling:* Acquisition of wrong patterns from following incorrect AI recommendations
- *Never-skilling:* Failure to acquire foundational competencies in the first place, because AI was present during training before skills were developed

**Why never-skilling is claimed to be worse than deskilling:**
- Deskilling is recoverable: if AI is removed, the clinician can re-engage practice and rebuild
- Never-skilling may be unrecoverable: the foundational representations were never formed; there is nothing to rebuild from
- Never-skilling is detection-resistant: clinicians who never developed skills don't know what they're missing; supervisors who review AI-assisted work can't distinguish never-skilled from skilled performance
- Never-skilling is prospective and invisible: it's happening now in trainees but won't manifest in clinical harm for 5-10 years, when current trainees become independent practitioners

**Evidence base (from this and related sources):**
- More than 1/3 of advanced medical students failed to identify erroneous LLM answers to clinical scenarios — calibration is already impaired
- Significant negative correlation found between frequent AI tool use and critical thinking abilities in medical students
- No prospective study yet comparing AI-naive vs. AI-exposed-from-training cohorts on downstream clinical performance

**Status of the concept in literature:**
- Formally named in NEJM (2025-2026), JEO (March 2026), Lancet Digital Health (2025)
- Articulated by NYU's Burk-Rafel as institutional voice
- ICE Blog commentary (August 2025): physician commentary by Raja-Elie Abdulnour MD amplifying the framing
- Still classified as: theoretical + observational correlations; no prospective RCT

## Agent Notes
**Why this matters:** Never-skilling has graduated from informal framing to peer-reviewed literature with a formal definition and explicit claim that it's a greater long-term threat than deskilling. This is the conceptual infrastructure needed to write the never-skilling claim in the health domain. The JEO source, combined with the NEJM and Lancet Digital Health citations, gives the claim a peer-reviewed foundation even though direct empirical proof is absent.

**What surprised me:** The orthopaedics literature is where this appears most explicitly — not radiology or internal medicine. The procedural nature of orthopaedics (where manual skills are central) makes it a natural context for never-skilling concern.

**What I expected but didn't find:** Any prospective study design attempting to test the never-skilling hypothesis. I expected at least one trial protocol. Not found. The conceptual literature is ahead of the empirical evidence, which is itself an important signal.

**KB connections:**
- Belief 5: Clinical AI creates novel safety risks requiring centaur design
- Existing claim on de-skilling and automation bias (should be enriched/linked)
- Theseus domain: AI safety, human-AI interaction risks
- Lancet editorial from Session 22 (also addresses this)

**Extraction hints:**
- Primary claim: "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, distinct from and arguably worse than deskilling"
- Confidence: EXPERIMENTAL — conceptually grounded, named in peer-reviewed literature, but no prospective empirical proof
- Note the detection-resistance argument as a key component: the risk is structurally invisible because neither the trainee nor the supervisor can detect what was never formed

**Context:** JEO is a Wiley-published orthopaedics journal. This likely appeared as a perspective/commentary piece rather than an original research study — the framing and language suggest editorial rather than empirical. Extractor should verify article type.

## Curator Notes (structured handoff for extractor)
PRIMARY CONNECTION: Existing clinical AI safety claims (deskilling, automation bias) in health domain; Theseus AI alignment domain
WHY ARCHIVED: Provides the peer-reviewed foundation for extracting the never-skilling claim as a distinct concept from deskilling; moves never-skilling from blog commentary to peer-reviewed literature
EXTRACTION HINT: Extract as a conceptual claim (EXPERIMENTAL confidence) — the argument for why never-skilling is worse than deskilling (detection-resistance, unrecoverability) is the core contribution, not empirical data
