Machine Learning is helping healthcare organizations improve operations and lower costs. By Michael Megarit


3 Ways Machine Learning is Transforming the Healthcare Experience


The digitization of society is well under way and the healthcare sector is no exception.

Since COVID, demand for medical services is booming and so is the need to capture, share, and deliver vast amounts of data quickly and efficiently.

Pharmaceutical companies and hospitals are looking for ways to increase productivity and ease the burden on healthcare workers.

As a result, the global Artificial Intelligence (AI) market for healthcare is growing at a phenomenal rate: it is forecast to reach $61.59 billion in 2027, compared to $3.39 billion in 2019.

Machine Learning, a common form of AI, is quickly transforming the practice of medicine by helping healthcare professionals diagnose patients more accurately, predict their future health and prescribe better treatments.


What is Machine Learning?


Machine Learning is “the use and development of computer systems that are able to learn and adapt without following explicit instructions, by using algorithms and statistical models to analyze and draw inferences from patterns in data”.

Here's How Machine Learning is Improving Healthcare Processes

In other words, a machine is fed structured data and learns how to analyze it independently, without requiring human intervention. The machine intelligently processes and categorizes vast pools of data, which speeds up statistical analysis and facilitates complex decision-making.

However, the principal limitation of Machine Learning is that the computer still thinks and acts like a machine – not like a human being. Thus, it is unable to perform complex tasks such as gathering data from images or videos.

This is where Deep Learning comes in.

Deep Learning is an advanced form of Machine Learning that tries to mimic how the human brain functions. Multi-layered neural networks are combined with different algorithms to allow unstructured data to pass between nodes in highly connected ways. Once programmed, the system is able to recognize and categorize the data without being updated or improved by a developer.

This means that the AI becomes smarter over time, all on its own.

While Machine Learning and Deep Learning are two slightly different things, we will refer to them both as “Machine Learning” for the purpose of this article.


How is Machine Learning used in healthcare?


Machine Learning can improve the accuracy of treatment protocols and health outcomes through algorithmic processes.

Simply put, it helps doctors diagnose diseases, predict future health issues and treat patients more accurately than ever before.

Here's How Machine Learning is Improving Healthcare Processes

Let’s explore these 3 applications:

  • AI for Medical Diagnosis

Machine Learning is especially useful in medical imaging and radiology. The computer’s fast processing speed and cloud infrastructure enable it to detect anomalies in images beyond what the human eyes can see.

This allows for more accurate and in-depth diagnosis.

For example, Machine Learning can detect, recognize and analyze cancerous lesions directly from images. This helps doctors save lives. By diagnosing cancer much earlier and helps patients save money that would have otherwise been spent on expensive treatments.

  • AI for Medical Prognosis

Medical prognosis is the branch of medicine that specializes in predicting a patient’s future health.

Machine Learning helps doctors analyze a patient’s past medical records, family medical history and current diagnosis. In turn, this helps to identify risks and calculate the probability of developing diseases.

For example, doctors can use Machine Learning to predict in-hospital mortality following amputations in diabetic patients. Depending on the results of the prognosis, they will take precautionary measures to tailor the surgery and recovery process. This ensures the patient’s safety and survival.

Again, this technology is invaluable as it allows doctors to elaborate personalized and specific healthcare plans for their patients, rather than following strict “by the book” protocols.

  • AI for Medical Treatment

Since Machine Learning is able to perform accurate diagnosis and prognosis, doctors know exactly what disease the patient has and which conditions he may develop in the future.

Thus, they can prescribe personalized and cohort treatments which improves the healthcare process and patient outcomes. Ultimately, medical procedures are streamlined, efficiency is scaled and costs for both hospitals and patients decrease.


Is AI the future of healthcare?


AI and Machine Learning applied to healthcare are still in their infancy.

As technology progresses, further advancements will continue transforming the industry.

Every year, there are major breakthroughs being made in all facets of healthcare, such as precision medicine, drug discovery and development, intelligent personal health records, ambient assisted living, and robotics and AI-powered devices that help doctors perform minimally invasive and ultra-precise surgeries.

These innovations will facilitate and enhance the quality of human work, rather than replace the work of physicians and other healthcare staff.


About the Author

Michael Megarit is a partner with Cebron Group. With over 25 years of domestic and international corporate finance experience, he provides M&A and capital advisory to high-growth technology companies.