Episode 43: How artificial intelligence can expand your range and depth of veterinary care


On this episode of The Vet Blast Podcast, Dr Adam Christman is joined by SignalPET founder Dr Neil Shaw, who explains how (and why) artificial intelligence (AI) is advancing veterinary radiology.

Upon his retirement from veterinary practice—the last 2 decades of which he spent in specialty care where he helped to establish the model of a referral hospital—Neil Shaw, DVM, DACVIM, started thinking about how treatment protocols in general practices could be improved. His primary concern: how to scale what is done in specialty practices for use in general practices.

Preventive care protocols are well established in general practice, Shaw says, but "models for treating common illnesses and injuries in primary care practice really have not been well established." “Not all cases need to be referred,” he tells Adam Christman, DVM, MBA, in this episode of the Vet Blast Podcast. And he saw technology as the only way to accomplish that goal.

About SignalPET

SignalPET was created to help clinicians gain more diagnostic value from traditional radiographs. Powered by patented artificial intelligence, SignalPET provides objective, reliable AI results on over 50 of the most common routine radiology findings seen by general and emergency veterinarians. Learn why veterinary professionals around the country are choosing SignalPET for high-quality radiology results that lower costs and save time at www.signalpet.com.

Artificial intelligence (AI) was being used in human medicine, so Shaw figured that was a good place to start in veterinary medicine, and he founded SignalPET, which offers advanced artificial intelligence technology in veterinary radiology for general practitioners.

Shaw and Christman also talk about the differences between AI results and radiology consultations, plus how AI can operate as a “second set of eyes” for radiologists in veterinary medicine.

A radiology consultation is broken down into 2 parts, Shaw says: identification of the image and recommendations on combining the clinical history, including other input, and laboratory results. Then the radiologist makes case recommendations.

On the other hand, the AI can recognize what’s on the image–allowing for recommendations based on history, signalment and input from veterinarians who submitted the films to occur sooner than later. “[AI] is really the bailiwick currently of radiologists and that’s what folks tend to rely on a radiologist for,” says Shaw.

According to Shaw, one of the most amazing aspects of AI in radiology is the diverse patient group, which often prompts this question: If the patient isn’t exactly straight or the position isn’t 100% perfect, would the system be able to tell normal from abnormal? Shaw notes that radiologists realized very early on all those challenges like species, size, and position did not affect the results at all.

“Think of the system as another clinician,” he says. “As long as the film is okay for another clinician, then the system can comfortably read the film.”

Listen below for more from Shaw on how AI in radiology could help your practice.

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