ReviewVeterinary research2021
Research perspectives on animal health in the era of artificial intelligence.
Review in Veterinary research, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 29 papers, 1 of them a synthesis that pooled 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.
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.
Who cites it
29 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Review of applications of deep learning in veterinary diagnostics and animal health.Frontiers in veterinary science · 2025Pooled it
- Artificial intelligence-driven advancements in agricultural biotechnology.Journal, genetic engineering & biotechnology · 2026Review
- Priorities and Recommendations for Using Artificial Intelligence (AI) to Improve Equid Health and Welfare.Animals : an open access journal from MDPI · 2026Article
- AI-Driven Multimodal Sensing for Early Detection of Health Disorders in Dairy Cows.Animals : an open access journal from MDPI · 2026Article
- Organic Pig Farming in Europe: Pathways, Performance, and the United Nations Sustainable Development Goals (SDGs) Agenda.Animals : an open access journal from MDPI · 2026Review
- Trends in artificial intelligence and machine learning for renal cancer.Discover oncology · 2025Article
- Raman spectral band imaging for the diagnostics and classification of canine and feline cutaneous tumors.The veterinary quarterly · 2025Article
- Applications and Considerations of Artificial Intelligence in Veterinary Sciences: A Narrative Review.Veterinary medicine and science · 2025Review
- Bulldogs stenosis degree classification using synthetic images created by generative artificial intelligence.Scientific reports · 2025Article
- Application of Genomic Selection in Beef Cattle Disease Prevention.Animals : an open access journal from MDPI · 2025Review
- Faces of time: a historical overview of rapid innovations in coding animal facial signals.Frontiers in veterinary science · 2025Review
- Article
- Improve animal health to reduce livestock emissions: quantifying an open goal.Proceedings. Biological sciences · 2024Article
- Comparison of Machine Learning Tree-Based Algorithms to Predict Future Paratuberculosis ELISA Results Using Repeat Milk Tests.Animals : an open access journal from MDPI · 2024Article
- Vet informatics and the future of drug discovery in veterinary medicine.Frontiers in veterinary science · 2024Article
- Expanding access to veterinary clinical decision support in resource-limited settings: a scoping review of clinical decision support tools in medicine and antimicrobial stewardship.Frontiers in veterinary science · 2024Article
- The potential application of artificial intelligence in veterinary clinical practice and biomedical research.Frontiers in veterinary science · 2024Article
- Human-computer interactions with farm animals-enhancing welfare through precision livestock farming and artificial intelligence.Frontiers in veterinary science · 2024Article
- Article
- From beasts to bytes: Revolutionizing zoological research with artificial intelligence.Zoological research · 2023Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
Abstract
Leveraging artificial intelligence (AI) approaches in animal health (AH) makes it possible to address highly complex issues such as those encountered in quantitative and predictive epidemiology, animal/human precision-based medicine, or to study host × pathogen interactions. AI may contribute (i) to diagnosis and disease case detection, (ii) to more reliable predictions and reduced errors, (iii) to representing more realistically complex biological systems and rendering computing codes more readable to non-computer scientists, (iv) to speeding-up decisions and improving accuracy in risk analyses, and (v) to better targeted interventions and anticipated negative effects. In turn, challenges in AH may stimulate AI research due to specificity of AH systems, data, constraints, and analytical objectives. Based on a literature review of scientific papers at the interface between AI and AH covering the period 2009-2019, and interviews with French researchers positioned at this interface, the present study explains the main AH areas where various AI approaches are currently mobilised, how it may contribute to renew AH research issues and remove methodological or conceptual barriers. After presenting the possible obstacles and levers, we propose several recommendations to better grasp the challenge represented by the AH/AI interface. With the development of several recent concepts promoting a global and multisectoral perspective in the field of health, AI should contribute to defract the different disciplines in AH towards more transversal and integrative research.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.