Machine Learning in Healthcare Can Be the Answer to Many Questions

Without much thought, it is easy to recall several movies, television shows and even books that have the premise around artificial intelligence or machines learning and becoming autonomous. Most of it seems so futuristic, however, it may be closer than you might think, and already a part of your daily life in ways that you can’t even imagine. One of the industries that could most benefit from machine learning is healthcare, but like most technology in business, healthcare is one of the furthest behind in implementing technology. Don’t count them out; machine learning in healthcare is making great strides and proving to be a perfect fit.

Whether you want to call it artificial intelligence (AI) or machine learning, it is basically the same thing. Many in the AI world make the distinction that AI is a branch of computer science dedicated to building the machines adept enough to perform intelligent tasks, while machine learning is the science of having computers think for themselves without human intervention. The healthcare community is looking toward machine learning much more than AI for treating and improving patient care, so we will make the differentiation here.

Part of the reason that the healthcare industry isn’t as advanced in incorporating machine learning is due to the complexity and diversity of healthcare data. It is very easy for Google, Facebook and Amazon to breakdown the data they are collecting and learn from customers utilizing their services because most everything is basic data and understanding customer needs. This is not the case for doctors and hospitals who deal with handwritten information, dictated memos, imaging results such as CAT scans and x-rays, and other test results. Drilling down through this sort of subjective kind of data doesn’t produce easy to correlate answers, yet it is exactly this kind of information that can help save lives.

Each of us would like to think that we are completely unique, but in so many respects we are far from being unique in the eyes of a medical professional. Most of us have the same symptoms when we get sick, we can be treated by the same types of medicine and we heal at roughly the same rate. But, it is impossible for physicians and other medical staff to know every aspect of every disease or illness and be able to capably handle the multiplicity of issues that come through their doors. This is exactly why having machine learning in healthcare is so essential. Even with commonality between patients, there are always varieties and unexpected aspects, as well as risk factors that aren’t always easily assessed, but with an outside entity that doesn’t work under any biases, mitigating components can be utilized to improve the patient’s chances for a healthy recovery.

What machine learning provides in a hospital, clinic or office is the ability to assess patients for risk level, know everything that the medical community has ever studied, understand and prescribe treatments that might be outside of the prevue of a treating physician. Overall, what this means is that machine learning can take the human error out of medical care, and improve the possible outcomes for each patient.

By no means can machine learning take the place of the human touch, interaction or emotion. Machines are simply tools that can be utilized to help, especially processing information that we are not able to handle all at once. This is especially important with an ageing population that is going to require more and more attention. Unfortunately, there aren’t going to be enough medical personal to supply the one-on-one assistance that is going to be required over the next decades, but with the help of machine learning and devices in the home that can provide the personal assistance needed. The device doesn’t have to be anything more than something along the lines of Amazon’s Alexa, just formatted to work with healthcare issues.

“The purest case of an intelligence explosion would be an Artificial Intelligence rewritinits own source code. The key idea is that if you can improve intelligence even a little, the process accelerates. It’s a tipping point. Like trying to balance a pen on one end – as soon as it tilts even a little, it quickly falls the rest of the way.” Eliezer Yudkowsky

Machine learning in healthcare has many inroads to make to become more mainstream. However, we are all doing our part to add to the vast amount of data that is necessary to know all about human health. Every time that we visit a doctor or medical facility, information is being gathered, processed and understood. As ways are found to better analyze and incorporate the collected data, and then translate that into ways in which machines and software can comprehend it, we as patients will be the beneficiaries of more accurate and efficient care.