Evidence map›Paper›PMID 41976060›Full record

ArticleAnimals : an open access journal from MDPI2026

Priorities and Recommendations for Using Artificial Intelligence (AI) to Improve Equid Health and Welfare.

Philippa L Young, Robert Hyde, Janet Douglas, Sarah L Freeman

Abstract read
In one paragraph

Article in Animals : an open access journal from MDPI, 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

4 authors.

Philippa L YoungSchool of Veterinary Medicine and Science, University of Nottingham, Loughborough LE12 5RD, UK.ORCID 0009-0006-9181-3753
Robert HydeSchool of Veterinary Medicine and Science, University of Nottingham, Loughborough LE12 5RD, UK.ORCID 0000-0002-8705-9405
Janet DouglasWorld Horse Welfare, Anne Colvin House, Norwich NR16 2LR, UK.ORCID 0000-0002-2980-5043
Sarah L FreemanSchool of Veterinary Medicine and Science, University of Nottingham, Loughborough LE12 5RD, UK.ORCID 0000-0002-3119-2207

Funding

Animal Welfare Research Network None
6 · The paper itself

Abstract

Artificial Intelligence (AI) is being increasingly used for equid health and welfare. This study aimed to establish consensus on where and how AI should be developed to achieve maximum benefit in this field. A workshop involving 41 stakeholders generated statements about current welfare concerns, areas for AI development, and barriers and solutions to AI use. Statements were circulated through Delphi surveys (acceptance set at 75% agreement). One-hundred-and-six statements reached agreement. Ethological needs not being met and poor equid management practices were key welfare concerns. Participants identified that insufficient owner/carer knowledge and understanding were important factors contributing to welfare concerns. Priority areas for AI development included assessment of equid wellbeing, as well as individual and population-level monitoring. Barriers included limited understanding of both equine behaviour and AI, biased, unethical, or insufficient data collection, difficulties developing accurate models, challenges to validation, and uncertainty around interpretation. Proposed solutions included development of evidence-based, unbiased AI systems, following best practice guidelines, requiring approval/regulation of AI tools, collaboration, and education of AI users. This is the first study to identify stakeholders' opinions about where AI is likely to have the greatest benefit for equids, potential barriers, and solutions. The findings should be used to prioritise funding and development.

Indexed as

artificial intelligencebarriersconcernsconsensusDelphiequidhorseprioritiessolutionswelfare

Identifiers

PMID41976060
PMCPMC13072182

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.