Artificial Intelligence and machine learning have made the jump from science fiction to reality. In fact, AI is fast becoming an integral part of many aspects of our lives, and the veterinary industry is no exception. Machine learning in veterinary practices is improving animal healthcare, making practices easier to manage and, most importantly, saving more lives. 

In this post we will take a look at how Artificial Intelligence is changing the landscape of veterinary care for the better. We will take a look at how machine learning can help vets improve the experience of both animals and pet owners alike. We will also share a few examples of machine learning in veterinary practices to give you a glimpse of how the latest technology is revolutionising the veterinary industry. 

What is Machine Learning?

Before we discuss how machine learning in veterinary practices is evolving, let’s take a look at a quick definition of what machine learning is, in more general terms.

Machine learning, also referred to as ML, is a subfield of Artificial Intelligence (AI) and computer science. The technique focuses on the use of statistical models and algorithms to imitate the way that humans learn. In other words, it relies on the learning of past experiences and data in order to draw conclusions and predict future behaviours. 

Machine learning is currently being used in a vast range of industries including education, medicine, banking, telecommunications, security, and bio-medical sciences. 

Machine Learning in Veterinary Practices

In human medicine, AI and machine learning are already being used in areas such as drug design, anaesthesiology, cardiology, radiology, oncology, and infectious disease management. And now we are also beginning to see practical applications of machine learning in veterinary practices that are saving lives. In fact, technological advancements in animal health care are quickly catching up with human health care. 

Domestic applications of machine learning include tracking, health and behaviour monitoring, and mechanisms for improved feeding and cleaning. Clinical applications include accurate diagnostics, and improvements to access to medical care and data collection. And the world of machine learning in veterinary practices is only just getting started.

Generally speaking, veterinary protocols for vaccinations, parasite prevention, and other aspects of animal wellness and care are already well-established. But there are other areas of the veterinary industry that are falling short, especially in terms of standardised models for treating sick or injured animals. Machine learning is helping to bridge this gap through the use of automated protocols and algorithms. Examples include the provision of primary veterinary care through smart kiosks. This enables vets to have access to reliable, consistent, and automated data that can help with diagnostics. And this, in turn, gives clinicians more time to focus on time management, patient care, and other aspects of their veterinary practice, enhancing the client and patient experience. 

Practical Applications of Veterinary Machine Learning

AI and machine learning are slowly revolutionising the veterinary industry. Machine learning is making it easier to diagnose health conditions, improving data collection mechanisms, and helping to make medical care more accessible.

 Here are a few practical applications of machine learning in veterinary practices:

  • Diagnostics: Machine learning applications can analyse images to learn parts of the anatomy and judge what is normal. Machine learning software can then identify suspected abnormalities. Diagnostic tools also help support the value of preventive care.
  • Patient Monitoring: Veterinarians can use AI and machine learning to monitor vital signs and eating and drinking behaviour in patients. Software can also notify veterinarians if anything unusual needs to be addressed.
  • Imaging: Machine learning is being used to tackle the shortage of veterinary imaging professionals. Machine learning technology can be used to analyse, interpret, compare and prioritise x-rays, streamlining the process for veterinarians. 
  • Surgery: Artificial Intelligence, robotics and machine learning are being used in operating rooms, revolutionising veterinary surgery.
  • Data analysis: Machine learning applications can be used to sift through large quantities of data to find complex patterns or unprecedented correlations.

Examples of Machine Learning in Veterinary Practices

Let’s take a look at a few examples of how the use of machine learning in veterinary practices is already changing the industry. These examples demonstrate the potential of ML technology and highlight how machine learning is already saving lives.

Cavalier King Charles Spaniels

Cavalier King Charles spaniels are predisposed to Chiari-like malformation (CM), a complex developmental condition of the skull and craniocervical vertebrae. A completely automated image mapping method has been tested by the University of Surrey Centre for Vision, Speech and Signal Processing (CVSSP) and the School of Veterinary Medicine. The aim was to discover patterns in MRI data that could help vets identify dogs that experience CM-associated pain. This technique will hopefully be developed as a diagnostic tool to help treat cavalier King Charles spaniels that are suffering from this disease. 

Diagnosing Cushing’s Syndrome in Dogs

Cushing’s syndrome is one of the most common endocrine diseases affecting dogs. What’s more, dogs with the syndrome are at increased risk of developing other diseases, such as diabetes mellitus, hypertension and pancreatitis. This makes speedy diagnosis vital. Despite this fact, the syndrome has historically been challenging to diagnose. 

To address diagnostic challenges, researchers at the Royal Veterinary College (RVC) in London are testing machine learning algorithms to support and improve the diagnosis of Cushing’s syndrome in dogs. The results are, so far, looking promising. Further development of these algorithms could lead to earlier, more reliable and cost-effective diagnoses and better clinical care for dogs with Cushing’s syndrome. This technology could also potentially be applied to other clinical areas.

How Will AI Change the Veterinary Industry?

It’s clear to see that AI and machine learning in veterinary practices are slowly changing the industry for the better. Advances in the automation of diagnostics, point-of-care testing and digital radiography are expanding the capabilities of veterinarians and improving efficiency levels. This is enabling practices to focus on patient care and maintaining the high clinical standards that clients and patients deserve. And, most importantly, all this is helping to save more lives. 

The impact these new emerging technologies will have on training, examining, diagnosing, treating and managing veterinary practices is likely to be profound. We have already seen how this technology is changing the way human doctors practice medicine, and there is no reason to believe that machine learning in veterinary clinics will be any less effective at saving the lives of animals.

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