ReviewVeterinary sciences2023
Artificial Intelligence in Veterinary Imaging: An Overview.
Review in Veterinary sciences, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
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
19 citing papers in PubMed.
- Agreement of Multimodal Large Language Models and Novice Readers With Expert-Based Radiographic Scoring of Presumptive Septic Carpal Arthritis in Calves.Veterinary radiology & ultrasound : the official journal of the American College of Veterinary Radiology and the International Veterinary Radiology Association · 2026Article
- Artificial Intelligence in Veterinary Neurology: Comparative Insights From Human Medicine and Cross-Species Technology Transfer.Veterinary medicine and science · 2026Review
- Bibliometric Analysis of Artificial Intelligence Use in Small Animal Thoracic Radiography.Veterinary medicine and science · 2026Article
- Deep learning for stenotic nares classification in brachycephalic dogs.Veterinary research communications · 2026Article
- Artificial Intelligence for the Diagnosis of Respiratory Diseases in Dogs and Cats: A Systematic Review.Veterinary sciences · 2026Review
- The influence, promise, and potential perils of artificial intelligence in veterinary medicine: a call for improved awareness and literacy.Journal of veterinary internal medicine · 2026Article
- Artificial intelligence models for point-of-care ultrasound diagnostics in dogs.Frontiers in veterinary science · 2026Article
- Radiomics and comparative neuroimaging of canine gliomas and human glioblastomas: a step toward precision medicine.Frontiers in veterinary science · 2026Article
- Enhancing dromedary camel (Frontiers in veterinary science · 2026Review
- Automated brain extraction for canine magnetic resonance images.BMC veterinary research · 2025Article
- Applications and Considerations of Artificial Intelligence in Veterinary Sciences: A Narrative Review.Veterinary medicine and science · 2025Review
- A Veterinary DICOM-Based Deep Learning Denoising Algorithm Can Improve Subjective and Objective Brain MRI Image Quality.Veterinary radiology & ultrasound : the official journal of the American College of Veterinary Radiology and the International Veterinary Radiology Association · 2025Article
- Toward a rapid, sensitive, user-friendly, field-deployable artificial intelligence tool for enhancing African swine fever diagnosis and reporting.American journal of veterinary research · 2025Article
- Advancing Veterinary Oncology: Next-Generation Diagnostics for Early Cancer Detection and Clinical Implementation.Animals : an open access journal from MDPI · 2025Review
- Based on digital intelligence: teaching innovation and practice of veterinary internal medicine in China's southwest frontier.Frontiers in veterinary science · 2025Review
- Subclinical Mastitis in Lacaune Sheep: Etiologic Agents, the Effect on Milk Characteristics, and an Evaluation of Infrared Thermography and the YOLO Algorithm as a Preprocessing Tool for Advanced Analysis.Veterinary sciences · 2024Article
- Advancements in Subchondral Bone Biomechanics: Insights from Computed Tomography and Micro-Computed Tomography Imaging in Equine Models.Current osteoporosis reports · 2024Review
- The potential application of artificial intelligence in veterinary clinical practice and biomedical research.Frontiers in veterinary science · 2024Article
- Neural Networks for the Detection of COVID-19 and Other Diseases: Prospects and Challenges.Bioengineering (Basel, Switzerland) · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
10 authors.
Funding
Abstract
Artificial intelligence and machine learning have been increasingly used in the medical imaging field in the past few years. The evaluation of medical images is very subjective and complex, and therefore the application of artificial intelligence and deep learning methods to automatize the analysis process would be very beneficial. A lot of researchers have been applying these methods to image analysis diagnosis, developing software capable of assisting veterinary doctors or radiologists in their daily practice. This article details the main methodologies used to develop software applications on machine learning and how veterinarians with an interest in this field can benefit from such methodologies. The main goal of this study is to offer veterinary professionals a simple guide to enable them to understand the basics of artificial intelligence and machine learning and the concepts such as deep learning, convolutional neural networks, transfer learning, and the performance evaluation method. The language is adapted for medical technicians, and the work already published in this field is reviewed for application in the imaging diagnosis of different animal body systems: musculoskeletal, thoracic, nervous, and abdominal.
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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.