Evidence map›Paper›PMID 42247373›Full record

ArticlePLOS digital health2026

Healthcare professionals show high AI enthusiasm but limited knowledge: A cross-sectional study.

Nada Abedin, Peter Hahn, Stefan Zeuzem, Georg Dultz

Abstract read
In one paragraph

Article in PLOS digital health, 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

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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

4 authors.

Nada AbedinMedical Clinic 1, University Hospital Frankfurt, Goethe University, Frankfurt am Main, Germany.ORCID https://orcid.org/0000-0002-3952-9855
Peter HahnVulpius Klinik, Bad Rappenau, Germany.
Stefan ZeuzemMedical Clinic 1, University Hospital Frankfurt, Goethe University, Frankfurt am Main, Germany.
Georg DultzMedical Clinic 1, University Hospital Frankfurt, Goethe University, Frankfurt am Main, Germany.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Artificial Intelligence (AI) is rapidly transforming medical practice, with successful integration critically depending on healthcare professionals' self-reported perceived knowledge, attitudes, and perceived barriers to adoption. Despite rapid technological advances, comprehensive assessments of healthcare professionals' AI readiness remain limited, particularly regarding the relationship between enthusiasm and self-reported competency. We conducted a cross-sectional online survey of 148 healthcare professionals. The survey assessed demographics, AI knowledge/attitudes (12 Likert-scale items), institutional readiness, learning preferences, and perceived barriers. Inferential statistics including correlation analyses, Kruskal-Wallis H tests, and Mann-Whitney U tests were performed to examine relationships between attitudes and contextual factors. Participants (mean age 36.7 ± 8.1 years, 61.5% male, 77.7% from Germany) demonstrated a significant knowledge-enthusiasm gap. While 86.5% believed AI will transform medical practice and 76.4% expressed excitement about AI changes, only 20.3% felt well-informed about healthcare AI and 38.6% had medical AI experience. Correlation analysis revealed strong positive associations among enthusiasm measures (r = 0.63-0.88, p < 0.001) but weak correlations between knowledge and enthusiasm (r < 0.20), providing evidence consistent with the knowledge-enthusiasm gap. Institutional AI stance significantly affected individual knowledge levels (Kruskal-Wallis H(3) = 28.11, p < 0.001), but not enthusiasm. Primary barriers included knowledge deficits among leadership (62.8% institutional level), infrastructure limitations (52.0%), and system integration challenges (57.4%). Healthcare professionals, particularly in a German healthcare context, demonstrate strong enthusiasm for AI integration but face significant knowledge gaps and institutional barriers. While these findings might be most directly applicable to similar healthcare contexts, the identified knowledge-enthusiasm gap represents a critical target for educational interventions in similar well-resourced European healthcare systems. Successful AI implementation requires multi-level strategies addressing leadership education, infrastructure development, and hands-on training programs. These findings provide evidence-based guidance for healthcare institutions, educators, and policymakers developing AI adoption strategies.

Identifiers

PMID42247373
PMCPMC13240888

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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.