Evidence map›Paper›PMID 42496257›Full record

Observational studyAdvances in respiratory medicine2026

Artificial Intelligence-Induced Deskilling in Interventional Pulmonology: An International Cross-Sectional Survey on Risk Perception and Mitigation Strategies.

Guido Marchi, Lorenzo Corbetta

Abstract readObservational Study
In one paragraph

Observational study in Advances in respiratory medicine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

2 authors.

Guido MarchiPulmonology Unit, Cardiothoracic and Vascular Department, University Hospital of Pisa, Via Paradisa 2, 56124 Pisa, Italy.ORCID 0009-0000-6122-7792
Lorenzo CorbettaDepartment of Experimental and Clinical Medicine, University of Florence, Viale Morgagni 63, 50134 Florence, Italy.ORCID 0000-0001-6733-4935

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Artificial intelligence (AI) is progressively reshaping interventional pulmonology (IP), yet its potential to erode procedural and cognitive competencies through AI-induced deskilling remains poorly characterized in this specialty. An international, observational, cross-sectional survey was conducted in May 2026 among 118 expert interventional pulmonologists from 10 different countries across 5 continents. Participants completed a structured questionnaire comprising five demographic items and 12 Likert-scale statements addressing deskilling risk perception and mitigation attitudes; percentage agreement was calculated for each item (scores 4-5). High perceived clinical value of AI was reported (87%), alongside substantial concern for procedural deskilling (73%) and upskilling inhibition (83%). Familiarity with automation bias was limited (38%), yet its clinical relevance was widely recognized after definition provision (81%)-a gap of 43 percentage points. Strong support emerged for AI-free training (84%), simulation-based training (86%), and longitudinal performance monitoring (78%). Concern for institutional fragility in the absence of AI was expressed by 74%, and governance frameworks, including minimum non-AI-assisted procedural volume requirements, were endorsed by 70%. Deskilling was identified as a high research priority by 89%. These findings indicate that AI-induced deskilling is perceived as a relevant and emerging risk by expert interventional pulmonologists internationally, even before the widespread clinical deployment of AI technologies. Although the extent to which these concerns will translate into measurable effects on procedural competence is currently uncertain, the results underscore the need for prospective research, educational initiatives, and appropriate governance frameworks to ensure the preservation of core procedural skills.

Indexed as

Artificial IntelligenceClinical CompetencePulmonary MedicinePulmonologistsCross-Sectional StudiesFemaleHumansMaleSurveys and Questionnairesartificial intelligenceautomation biasbronchoscopydeskillinginterventional pulmonologymedical educationprocedural competencesimulation-based trainingupskilling inhibition

Identifiers

PMID42496257
PMCPMC13398125

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.