Data analysis

Thanks to continuous improvements in modern medicine, veterinarians are inundated with data from devices, software, and other sources. While more is certainly better, it can also be overwhelming; large datasets are difficult for humans to read. They also might contain irrelevant data or false patterns. 

Also, when faced with a firehose of data, a human being is likely to miss the big picture. Not so with AI, which never tires, and can sift through large quantities of data to find complex patterns, unprecedented correlations, or small abnormalities humans cannot see.

For example, consider some recent work done by veterinarians at the University of California, Davis School of Veterinary Medicine,³ who worked with a computer engineer to develop an algorithm tasked with finding Addison’s disease in dogs. 

Addison’s is a rare disorder, potentially fatal because it mimics the symptoms of other diseases. This means it’s often misdiagnosed, going undetected and untreated for years. Dogs with Addison’s present with vague symptoms that look like other conditions, such as kidney and intestinal disease.

Normally, when a sick patient first visits the vet, routine blood tests are ordered — a complete blood count and serum biochemical profile. Because Addison’s patients lack critical hormones, their tests often come back with subtle irregularities that are frequently confused with other conditions. 

The UC Davis team’s algorithm uses AI to analyze blood work data and detect complex patterns unique to Addison’s. The researchers used the test results of 1,000 dogs to train their algorithm to detect the patterns that signal Addison’s. 

The algorithm functions as an alert system, using information from routine screening tests to flag patients in which Addison’s disease is likely, and inform veterinarian that further investigation is necessary. It has been 99% effective in diagnosing new patients.