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What Machine Learning Means for Companies in the Healthcare Industry

by | Feb 15, 2020

What Machine Learning Means for Companies in the Healthcare Industry

There’s a lot of hype around machine learning right now and it’s potential to revolutionize our world. One area where machine learning is showing promise is in the healthcare industry. For companies in the healthcare industry, executing effective machine learning applications could totally change how patients receive care.

Nevertheless, creating a successful machine learning application is still a major challenge, with the majority of AI projects not delivering the expected results. As such, companies in the healthcare industry need to pay close attention to the areas in which AI is most likely to produce outcomes. Highlighting the low hanging fruit and current ML initiatives in healthcare is the focus of this article.


Supporting Clinical Decisions

It’s not likely that AI will replace doctors anytime soon. While that science fiction tale has been told many times, there are simply too many intuition and problem solving steps doctors still use when treating patients that algorithms can’t yet reproduce. In addition, bedside manner and emotional intelligence and communication are critical for healthcare professionals, and AI is unable to reproduce the experience of talking directly with a doctor who can empathize with you.

That said, the early applications of machine learning in healthcare will support human doctors so they can make better decisions. There’s a lot of healthcare data out there, and the low hanging fruit for machine learning in healthcare is helping doctors identify patterns and potential explanations. 

An AI algorithm can suggest possible explanations and solutions to healthcare problems along with a confidence level of the likelihood of each explanation. Then, the doctor can use his/her training and intuition to choose from those options. As more data comes in from the patient, the algorithm can update its predictions and monitor condition for anomalies, leading to more accurate and punctual care.


Diagnostics & Identification of Disease

As part of supporting human healthcare professionals, algorithms will play an increasingly large role in identifying and diagnosing disease. Already, machine learning algorithms can successfully identify skin cancer from images of patients’ skin. Additionally, algorithms can identify other diseases and anomalies in diagnostic images like MRIs, CT scans, PET scans, ultrasounds, and x-rays.

For many of these scans, correctly reading and identifying issues is a difficult task for human professionals. With the help of algorithms, radiologists and other professionals can get an initial opinion on areas of concern. An algorithm can point out areas that the radiologist might have missed, leading to more comprehensive diagnostic results.

Blood and urine testing are other areas where AI can be equally helpful in identifying issues. Often, health issues involve complex interactions between several markers in the body. AI is especially good at recognizing complex patterns and could help healthcare professionals identify combinations they might have missed.


Tailored Treatment Plans

Another exciting area for machine learning is on the side of treatment and ongoing care.

Right now, doctors must prescribe many drugs and treatment plans in a one-size-fits-all fashion. With machine learning, it could become easier for doctors to write prescriptions based on height, weight, sex, past medical history, and other factors. Not just prescriptions, but also diet plans, exercise regimens, and more could be customized to fit individuals.

Treatment customization is a growing and important trend that will lead to improved health outcomes for patients and faster recovery times.


Pharmaceutical Development

Machine learning is already showing promise in the area of pharmaceutical development. Using machine learning, pharmaceutical companies can predict the ways that compounds will react and affect the body for different types of patients. Some algorithms model the chemistry while others rely on analyzing public data from past clinical trials across many pharma companies

The result is a faster, cheaper development cycle for new drugs with less reliance on human trials to identify side effects and problems with compounds.


Machine Learning is Changing Healthcare

The applications of machine learning in healthcare are varied and exciting. As these algorithms improve, they’ll boost the quality, speed, and effectiveness of care for millions of people. Healthcare companies that use machine learning will face challenges on initial implementation, to be sure, but for those that stay the course, the benefits of well-developed algorithms promises to be enormous.

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