Evidence map›Paper›PMID 42639421›Full record

ArticleCureus2026

Addressing Clinician-Educator Hesitancy Toward Artificial Intelligence Through a Peer-Led Instructional Design.

Nathan Wilson

Abstract read
In one paragraph

Article in Cureus, 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

1 author.

Nathan WilsonPediatrics, Augusta University, Augusta, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

introductionArtificial intelligence (AI) has had a tremendous impact on medical education. National data are indicative of massive clinical interest and usage, but a "faculty readiness gap" exists among experienced clinician-educators. Though closing this gap is essential, traditional training models often fail to improve appropriate application of AI tools. This study describes medical education development, innovation, and competency in AI (MEDIC-AI), an instructional method designed to increase faculty readiness through peer modeling of AI applications critical to success.

methodsA physician-educator (NW) at the Medical College of Georgia (MCG) at Augusta University created a peer-led instructional platform to explain and model the use and application of AI tools. The pilot episode was distributed anonymously to colleagues, and perceived usefulness (PU), perceived ease of use (PEOU), and behavioral intention (BI) to implement at least one AI tool within 30 days were measured using a cross-sectional survey based on the Technology Acceptance Model (TAM).

resultsEvaluations from 33 faculty demonstrated substantial acceptance, reporting high PU (90.9%) and PEOU (81.8%) regarding the format, and 87.9% demonstrated BI. Qualitative data suggest that peer explanation and modeling were critical to the results.

conclusionUsing AI can simplify workflow for busy physician-educators. When AI applications are simply and concisely explained and demonstrated by a trusted colleague hosting a do-it-yourself (DIY) format, participants reported increased usefulness, ease of use, and intent to implement. This suggests that incorporation of peer leadership into traditional instruction on AI tool use has the potential to close the "faculty readiness gap," improving teaching and patient care.

Indexed as

artificial intelligence in healthcareartificial intelligence in medicineeducator training programfaculty development in medical educationteaching technology

Identifiers

PMID42639421
PMCPMC13501485

What OpenQuestion holds

Textmetadata
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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.