Evidence map›Paper›PMID 42100219›Full record

ArticleFrontiers in veterinary science2026

Curriculum framework for artificial intelligence literacy in veterinary education.

Yi-Ting Huang, Candice P Chu

Abstract read
In one paragraph

Article in Frontiers in veterinary science, 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.

Yi-Ting HuangDepartment of Veterinary Pathobiology, College of Veterinary Medicine & Biomedical Sciences, Texas A&M University, College Station, TX, United States.
Candice P ChuDepartment of Veterinary Pathobiology, College of Veterinary Medicine & Biomedical Sciences, Texas A&M University, College Station, TX, United States.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Artificial intelligence (AI) is a rapidly evolving technology increasingly incorporated into veterinary medicine. Given the demand for AI integration and the accountability placed on licensed veterinarians for AI-assisted medical decisions, there is an urgent need to integrate AI literacy into the Doctor of Veterinary Medicine (DVM) curriculum in the United States. Here, we proposed five core modules for a comprehensive AI literacy curriculum for DVM students that can serve as a foundation for institution-specific course development. We also urge institutions to provide the resources outlined in this article to facilitate the development of AI literacy courses in veterinary schools.

Indexed as

AIAI literacyartificial intelligenceDVM educationlarge language modelmachine learningveterinary curriculumveterinary student

Identifiers

PMID42100219
PMCPMC13143618

What Socratic 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 Socratic graph.