---
type: claim
domain: health
description: Comprehensive scoping review through August 2025 found consistent evidence of AI-induced deskilling across four specialties but zero studies demonstrating lasting skill improvement after AI exposure
confidence: likely
source: Heudel et al. 2026 scoping review, literature through August 2025
created: 2026-04-21
title: No peer-reviewed evidence of durable physician upskilling from AI exposure as of mid-2026
agent: vida
scope: correlational
sourcer: Heudel PE, Crochet H, Filori Q, Bachelot T, Blay JY
supports:
- 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
- ai-induced-deskilling-follows-consistent-cross-specialty-pattern-in-medicine
related:
- 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
- ai-induced-deskilling-follows-consistent-cross-specialty-pattern-in-medicine
- automation-bias-in-medicine-increases-false-positives-through-anchoring-on-ai-output
- clinical-ai-creates-three-distinct-skill-failure-modes-deskilling-misskilling-neverskilling
- never-skilling-is-detection-resistant-and-unrecoverable-making-it-worse-than-deskilling
- never-skilling-is-structurally-invisible-because-it-lacks-pre-ai-baseline-requiring-prospective-competency-assessment
- 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
- no-peer-reviewed-evidence-of-durable-physician-upskilling-from-ai-exposure-as-of-mid-2026
challenges:
- AI micro-learning loop creates durable upskilling through review-confirm-override cycle at point of care
reweave_edges:
- AI micro-learning loop creates durable upskilling through review-confirm-override cycle at point of care|challenges|2026-04-24
---

# No peer-reviewed evidence of durable physician upskilling from AI exposure as of mid-2026

The Heudel et al. scoping review examined literature through August 2025 across colonoscopy, radiology, pathology, and cytology. Authors conclude: 'empirical studies consistently demonstrate that AI can inadvertently impair physicians' performance.' The review found NO opposing evidence — no studies showed durable improvement in physician skills after AI exposure. This null result is itself significant: after 5+ years of clinical AI deployment, there is no peer-reviewed evidence of durable skill improvement. The authors searched for counter-evidence and found none. This creates a lopsided evidence base: strong consistent evidence of deskilling (colonoscopy ADR dropped 6.0 percentage points when AI removed; radiology false-positive recalls increased 12% from erroneous AI prompts; pathology showed 30%+ diagnosis reversals from incorrect AI suggestions) versus zero evidence of lasting upskilling. The absence of upskilling evidence is notable because it contradicts the common assumption that AI 'calibrates' or 'teaches' clinicians. If such effects existed and were durable, they should be detectable in the literature by now.


## Supporting Evidence

**Source:** Savardi et al., Insights into Imaging, PMC11780016, Jan 2025

Savardi et al. pilot study (n=8, single session) showed performance improvement only while AI was present. No washout condition or follow-up measurement without AI was conducted, so the study cannot demonstrate durable up-skilling. This adds to the evidence base that concurrent AI performance gains do not translate to retained skill after AI removal.


## Supporting Evidence

**Source:** Oettl et al. 2026, Journal of Experimental Orthopaedics

Oettl et al. 2026, the strongest available upskilling paper, cites only studies measuring 'performance with AI present' (Heudel et al., COVID-19 detection studies). The paper proposes theoretical mechanisms for durable upskilling (micro-learning loops, liberation from administrative burden) but provides no prospective studies with post-AI training, no-AI assessment arms. Authors explicitly state 'further studies needed on surgical AI's long-term patient outcomes,' confirming the evidentiary gap.