LifeLearn Sofie: A Cognitive Veterinarian’s Assistant

Like human medicine, veterinary medicine has leaped into the digital age, embracing big data, telemedicine, online access for customers, online education for practitioners, digital marketing, and social media. Both sets of practitioners are also under increasing pressure to handle more patients in less time, and to keep up with a growing body of research that becomes outdated quickly.

However, there are some key differences between human and animal medical practitioners. Complex as human medicine is, it still targets only one species. Veterinarians, however, must be prepared to deal with everything from anacondas to zebras, and conditions that range from general wellness and internal medicine to cardiology, oncology and beyond. And, their patients can’t talk.

LifeLearn is a spin-off from the University of Guelph’s Ontario Veterinary College in Ontario, Canada. When they were founded 21 years ago, it was with the goal of providing educational and support services, resources, technology and tools to veterinary practices. As the field has evolved, though, so have they. LifeLearn’s Innovations Group is betting on new technologies like digital monitoring devices for animals to provide solid data on patients.

When the chance to partner with IBM’s Watson came along, it seemed to Jamie Carroll, LifeLearn’s CEO and Dr. Adam Little that creating a better digital assistant could solve some of the problems that veterinarians face today. LifeLearn is one of the first partners selected by IBM Watson and is using the technology to develop a cognitive veterinary assistant, called LifeLearn Sofie™, that can ingest massive amounts of data, and forage in real time for clues and connections that will allow a veterinarian to diagnose an animal’s condition quickly and accurately. Like other Watson-based assistants for physicans being developed, LifeLearn’s Sofie is training a veterinary version of Watson that uses the information it has amassed and analyzed to generate evidence-based hypotheses and suggest the best treatment options.

Preparing the content for Watson has been a massive undertaking. Working with leading hospitals, LifeLearn has reduced that process from weeks to hours. The LifeLearn staff have also had to train Watson to answer nuanced complex questions for which there is no single right answer. For each topic, their Watson trainers must create a set of questions that would be germane to a vet working through a case. They are now able to produce 25,000 question/answer pairs per month.

LifeLearn has built not just the underlying knowledge base, but analyzed how veterinarians gather and use information. Based on their decades of experience, they have developed an interactive application that enables veterinarians to ask questions and receive the top answers that are scored for confidence. The system learns from each interaction, and from feedback from users, who are asked to score the responses for relevance, quality of information, appropriate length and depth of answers.

LifeLearn’s goal is to make Sofie a specialist in every corner of veterinary science. To succeed, they must uncover how veterinarians make decisions. But there is an additional challenge: to educate veterinarians to understand the promise and limitations of cognitive computing—that there is no right answer, only some that are more appropriate than others, given the patient, its owner, and the circumstances of the medical condition. Living with uncertainty and complexity, and providing guidance in how to do this as well as possible is the aim of applications like LifeLearn’s Sofie.

Leave a Reply

Fill in your details below or click an icon to log in:

WordPress.com Logo

You are commenting using your WordPress.com account. Log Out / Change )

Twitter picture

You are commenting using your Twitter account. Log Out / Change )

Facebook photo

You are commenting using your Facebook account. Log Out / Change )

Google+ photo

You are commenting using your Google+ account. Log Out / Change )

Connecting to %s