{"id":11518,"date":"2022-02-03T15:41:15","date_gmt":"2022-02-03T15:41:15","guid":{"rendered":"https:\/\/todaysveterinarybusiness.com\/?p=11518"},"modified":"2023-05-22T22:22:03","modified_gmt":"2023-05-22T22:22:03","slug":"artificial-intellience-evolution","status":"publish","type":"post","link":"https:\/\/navc.sitepreview.app\/todaysveterinarybusiness.com\/artificial-intellience-evolution\/","title":{"rendered":"The AI Evolution"},"content":{"rendered":"

Veterinarians have consistently applied the newest advances in technology and medicine to animal health, from digital imaging to sophisticated anesthesia. So, it\u2019s no surprise they have turned to artificial intelligence to improve patient care.<\/p>\n

At its most basic level, AI is the simulation of human intelligence by computers. This doesn\u2019t mean (yet) that the computer thinks and learns as you might see in popular culture. Rather, AI means software that learns and makes decisions, often quicker and more accurately than human users. This makes AI ideal for complex tasks requiring the analysis of a large quantity of data and the sorts of repetitive tasks that might cause humans to make mistakes.<\/p>\n

Because AI is excellent at data-oriented tasks, it has the potential to revolutionize veterinary medicine in many ways. In fact, AI is being used in various facets of animal health to help veterinarians diagnose, treat and make better decisions about patient health.<\/p>\n

Imaging<\/h3>\n

The veterinary industry suffers from a shortage of imaging professionals. Universities have been hit hard, and even the more lucrative private practices struggle to find and keep veterinary radiologists<\/a>. The shortage is likely to worsen given the difficulty of universities in finding qualified instructors to train the next generation of imaging professionals. This is where AI can help.<\/p>\n

Today, two startups (SignalPET and Vetology AI) offer AI products designed to interpret X-ray images. In addition, a European team is applying similar technology to magnetic resonance imaging by using a trained convolutional neural network (CNN). A type of computing network inspired by the human brain, CNN detects the differences in canine brain lesions. In this case, the research is not necessarily based on a lack of MRI technicians but instead on the fact that distinguishing between meningeal-based and intra-axial lesions is challenging. Quick and accurate diagnosis, however, is vital if the patient is to receive the correct treatment.<\/p>\n

AI is well suited to radiology because it can quickly compare images, prioritize data and analyze information-filled images. Veterinary radiologists won\u2019t necessarily be rendered obsolete, but AI can streamline the analysis process, filtering out unremarkable images so that human doctors concentrate on the images that most need attention.<\/p>\n

Data Analysis<\/h3>\n

Modern medicine puts more data than ever at veterinarians\u2019 fingertips. While the data can benefit clinicians and their patients, it also can be overwhelming. Irrelevant data, false patterns and the tedium of reading data cause a human to tire and make mistakes. AI never tires and can sift through large quantities of data to find patterns that people cannot see.<\/p>\n

Take, for example, work done by veterinarians at the University of\u00a0California, Davis, School of Veterinary Medicine. Drs. Krystle Reagan and Chen Gilor worked with a computer engineer to develop an algorithm that detects Addison\u2019s disease<\/a>. Addison\u2019s, which is rare and potentially fatal, mimics the symptoms of other conditions. Therefore, it\u2019s often misdiagnosed. The UC Davis team\u2019s algorithm uses AI to analyze blood work and detect complex patterns unique to Addison\u2019s. The algorithm is 99% effective at diagnosing new patients.<\/p>\n

Another AI application involves notes, which many veterinarians take when a patient comes in. Although they\u2019re filled with important data and insights, the notes are often written\u00a0in a narrative format without diagnostic codes. Narratives are easy for humans to read but can be difficult\u00a0for machines, which use values input\u00a0into fields.<\/p>\n

A team of Stanford Medicine researchers is using AI to solve the problem. Their algorithm, called DeepTag, reads a veterinarian\u2019s typed notes and assigns codes representing ailments or symptoms. Based on a dataset of thousands of annotated veterinarian notes, the algorithm then predicts diseases that might be relevant to the patient.<\/p>\n

Diagnosis and Prediction<\/h3>\n

Some life-threatening diseases require more than a diagnosis. Take chronic kidney disease<\/a> in cats. CKD isn\u2019t reversible, and often, by the time it\u2019s diagnosable, the patient has sustained considerable kidney damage. However, if the disease can be predicted before it develops, the patient can be treated before any damage occurs. Researchers developed an algorithm that uses AI to predict whether a cat will develop CKD within two years. Trained on data from 20 years of veterinary visits, the algorithm is 95% accurate.<\/p>\n

Cushing\u2019s syndrome is another disease that often stymies veterinarians. Because dogs with Cushing\u2019s present a range of symptoms that might be attributable to other diseases, a British team is using four algorithms to analyze patient data and predict a Cushing\u2019s diagnosis in their canine patients.<\/p>\n

Prediction isn\u2019t limited to forecasting whether an animal will develop a disease or how the disease will progress. It also can tell a veterinarian which drugs will be most effective against a disease.<\/p>\n

The treatment of blood cancers in dogs, for example, can benefit from AI. While chemotherapy is often the treatment of choice in cancer cases, finding the right drugs to use in each patient is a difficult task. In addition, the treatment is costly and time-consuming and takes a toll on the patient and its owner. AI can help veterinarians screen the most effective drugs and exclude ineffective ones before choosing a treatment regimen.<\/p>\n

Will AI Replace Veterinarians?<\/h3>\n

Trepidation around AI often centers on a fear that artificial intelligence will replace people. That\u2019s not the case here. Nothing can replace the expertise of a good veterinarian in the diagnosing and treating of animal patients. However, AI is a valuable tool that can help veterinarians do their jobs with greater speed and precision, freeing them to concentrate on more complex tasks and focus on the welfare of their patients.<\/p>\n

\n

A BACKUP PLAN<\/span><\/h3>\n

DeepTag, a classification algorithm for veterinary clinical text, isn\u2019t omniscient, its developers acknowledged in a study published at go.nature.com\/2RpLeoT<\/a>. \u201cDeepTag also learns to abstain in examples when it is uncertain and defers them to human experts, resulting in improved performance,\u201d their article states.<\/p>\n","protected":false},"excerpt":{"rendered":"

Veterinarians have consistently applied the newest advances in technology and medicine to animal health, from digital imaging to sophisticated anesthesia.<\/p>\n","protected":false},"author":379,"featured_media":11519,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"iawp_total_views":0,"footnotes":""},"categories":[299],"tags":[],"class_list":["post-11518","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-february-march-2022","column-innovation-station","clinical_topics-innovation-technology"],"acf":{"hide_sidebar":false,"hide_sidebar_ad":false,"hide_all_ads":false},"yoast_head":"\nThe AI Evolution | Today's Veterinary Business<\/title>\n<meta name=\"description\" content=\"Artificial intelligence is a valuable tool that can help veterinarians do their jobs with greater speed and precision.\" \/>\n<meta name=\"robots\" content=\"noindex, follow, max-snippet:-1, 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