If you are an avid follower of technology news, you can’t help but have heard the term “AI”. AI stands for Artificial Intelligence, a field of technology pioneered by Alan Turing when he created a machine to break the German’s Enigma Code during World War II. The end goal for those developing AI will be the creation of a sentient machine that can think like a human being. Needless to say, that is still a long way off. However, during the course of 2016, the field of AI saw many advancements and investments, and the large number of those were related to healthcare applications.
Why is that?
In order to understand exactly how technology can contribute to better patient outcomes, we need to look beyond the vision of the sentient robot and focus in on two very specific areas where AI can assist medical professionals today.
The first area of AI that is immediately accessible to today’s healthcare professional is predictive analytics or the use of a computer to analyze large amounts of data to make recommendations on care or predict potential health issues before they arise. Analyzing data means that you must first be collecting it and providing access to the data to the system in question.
The proliferation of devices that collect patient data has already begun. There are wearables like the familiar Apple Watch or FitBit, but there are also implantable and ingestible medical sensors that can relay information on everything from blood sugar and oxygen levels to the frequency of asthma attacks in the varying quality of air. We put together a list of some interesting sensors last year, but one thing is for certain: The ability to collect and communicate patient health data is only increasing.
Of course the course of treatment is still determined by a living, breathing doctor, but having a relevant, data driven history of the patient’s health only makes that diagnosis and treatment plan better.
Deep Learning takes over where predictive analytics stops. According to one definition,
“Deep Learning is a branch of machine learning based on a set of algorithms that attempt to model high-level abstractions in data by using model architectures, with complex structures or otherwise, composed of multiple non-linear transformations.”
That definition, being as clear as mud, can really be boiled down to this. Deep Learning allows computers to analyze not only the data, but also to catalog the human responses to that data, allowing the computer to reach its own conclusions (learn).
Predictive analysis would say that a patient has high sodium that may lead to hypertension and should be assessed for cardiac health, where deep learning would go a step further and recommend potential treatments based on the treatment of other patients that fit the same profile.
Again, deep learning requires that both the data from the patient and the response of the physician are both accessible in order to draw these correlations. Again, deep learning is not a replacement for the experience and discernment of a physician, but can be helpful in situations where a doctor is not yet available. According to Forbes’ Bernard Marr, 30% of providers will use this type of technology by 2018. He even puts forth an example of a patient visit that leverages deep learning.
“Imagine walking in to see your doctor with an ache or pain. After listening to your symptoms, she inputs them into her computer, which pulls up the latest research she might need to know about how to diagnose and treat your problem. You have an MRI or an x-ray and a computer helps the radiologist detect any problems that could be too small for a human to see. Finally, a computer looks at your medical records and family history and compares that with the best and most recent research to suggest a treatment protocol to your doctor that is specifically tailored to your needs.”
Now imagine the added benefit of the same technology to those in a remote location who need emergency care and are able to receive it via telemedicine.
In discussions on healthcare reform, there are always discussions about breaking down artificial barriers between patients and caregivers to deliver better and more efficient care. However, this may be one instance when adding an artificial layer, in the form of technology using artificial intelligence, may actually be of benefit to both the patient and the physician.
Avidex AV is revolutionizing the way healthcare facilities and doctors are delivering care. Their 20 years of experience is being leveraged to drive down the cost of care while promoting positive healthcare outcomes. Is your organization looking for a new kind of technology partner? Connect with one of our Account Executives today to learn more.
About Jeff Miller
Jeff has been working in the professional AV integration industry for over twenty years. During that time he has served as Designer, Project Manager and/or Account Executive for hundreds of projects. As an Account Executive at Avidex, he specializes in Medical, Education, and Control Rooms. He can be reached at email@example.com