6 Ways Machine Learning Is Changing Healthcare

Machine learning is changing the way we live, impacting everything from the tech industry to agriculture, insurance, banking, and marketing. One of the domains where machine learning is having the most significant impact is healthcare.

Machine learning applications in healthcare essentially combines the processing power of millions of human minds to accelerate and reinvent such fields as diagnostics and medicine, changing the way we live and increasing the average lifespan.

In this guide we’ll have a look at six ways machine learning and artificial intelligence is changing the healthcare industry, helping it to evolve into something that would have seemed unfathomable just a decade ago. We hope the implementation of machine learning in healthcare could make the future brighter for generations to come.


One of the age-old complaints about the healthcare industry is that there aren’t enough drugs out there to treat the ever-growing list of diseases. The conspiracy theorists would have you believe that this is the result of pharmaceutical companies preferring to stick with current patents and to sell off old stock. They’d also make you think that these companies have a stockpile of treatments lying in wait.

The truth is, it costs an average of $2.6 billion to develop a new drug, with the vast majority of money being spent on the early stages of development, long before the drugs are sent away for regulatory approval. For a single treatment to be found, over 10,000 compounds will be studied and then whittled down to just 10 or 20, after which more tests will be done and eventually they’ll have one or two compounds that prove to be the most effective.

This is a long and complicated process, and it is also not an exact one. In discarding thousands of compounds in a search for a new blood pressure medication, researchers could be dumping a cure for cancer, malaria, AIDS, or another deadly disease.

Hopefully, machine learning is changing this industry, making the drug discovery process cheaper and more refined potentially leading to better treatments making it to market. Pfizer, one of the biggest pharmaceutical companies in the world, is using IBM Watson, which utilizes machine learning, to search for the next generation of treatments. Similar systems are also being used by other pharmaceutical giants and are set to change the way new meds are found.

These systems analyze thousands of compounds, as well as the diseases they are being designed to treat, to find new drug formulas.


The London-based firm BenevolentBio has created an advanced SI that utilizes machine learning to peruse vast amounts of data relating to diseases, symptoms, causes of death, and more. The machine learning models are trained with data from patient records, clinical trials, research papers, and drug data to create a detailed analysis of the relationships between symptoms and diseases, as well as the drugs prescribed to treat them.

The insights are available for researchers to study a correlation between patient data and its diseases. This information simplifies the process of finding new cures and detecting new infections, as well as helping researchers to track possible pandemics and to understand better why some diseases are prevalent in specific cultures and demographics.


Modern hospitals are hi-tech environments run by expensive machines and the trained staff that know how to use them. The hospitals are steadily shifting towards automation, to a future where diagnoses can be made quickly and accurately. Machine learning can accelerate disease diagnostics and make this process more and more accurate.

Hospitals produce over 50 petabytes of data every single year, a lot of which consists of medical images and meta-data. Unfortunately, they don’t have the means to record and analyze all of this data. It was a regular situation when a collected data was going to waste, or just being stored into a self-made data center inside the hospital. With the rise of machine learning, the situation dramatically changed.

Today, all the medical data collected through the years can be processed by advanced machine learning algorithms in search of insights. The gathered insights are used to understand the human condition better and to get acquainted with a host of diseases.

In 2016, for instance, a team of researchers from the United Kingdom processed thousands of eye scans to create a system that could accurately diagnose eye conditions. The result was a tool that could correctly predict the disease with 94% accuracy and is being used as a first-course treatment, referring a patient to the proper department.

Medical knowledge is gathered through experience — if a doctor sees a condition multiple times, they’ll know how to recognize it in the future. But while a doctor could spend a lifetime analyzing enough eye scans to understand this part of the anatomy truly, a machine can process the same number of scans in a matter of hours.

These systems are also being used to process MRI Scans, CAT scans, and other data. Tremendous advancements have been made in a short space of time.


Machine learning in healthcare is also being used to predict the chance of miscarriage accurately, stillbirth and pregnancy complications by processing data from thousands of successful and unsuccessful pregnancies and using this to create a series of probabilities.

These systems can process everything from HSG results to previous births/miscarriages before processing this along with a woman’s age, weight, and medical conditions. By inputting a new patient’s data into this system, it can return a series of recommendations based on previous outcomes, therefore increasing the chance of a successful birth and a healthy baby and mother.


Machine learning is also being used to make healthcare systems more effective, eradicating the need for constant professional support and moving toward a future where a virtual assistant will ultimately provide help.

AI-based chatbots and ML-powered virtual assistants are providing some much-needed assistance to patients who are old, infirm or living in rural areas without easy access to healthcare. These citizens can be connected to virtual health assistants known as Intelligent Virtual Assistants (IVA) or Medical Virtual Assistants (MVA) to receive nearly the same level of care they would receive if they were sitting in the doctor’s office.

These systems range in complexity. At their core they create a bridge between the patient and the doctor, allowing the former to receive help and giving the letter a chance to provide that help without wasting their time making and keeping appointments.

Virtual assistants are already being used throughout the US, and they are becoming more and more popular as the need for them arises. From year to year implementation costs are reducing, so it makes it possible for almost any healthcare company to build one.

Millions of people worldwide trust their health to wearable devices and IoT, that monitor their vitals. These devices rely on various sensors and the data these sensors provide. Machine learning is known for its data analysis capability, especially real-time sensor data analysis. There are a considerable number of applications that can alert the host or a family doctor if something goes wrong.

In the future, a patient may only need to download an app and apply a wearable to get the help they need. The wearable will then track their vitals as the app records them and advises on the best course of action, whether that be a referral or essential dietary advice. The intelligent virtual assistant for healthcare empowered by artificial intelligence is not a fiction but is a reality.


Machine learning in healthcare can not only help to find new cures in the compound database pharmaceutical companies have at their disposal, but it also helps to find out how well-tested drugs can cure existing diseases.

Daniel Cohen is one of the men behind this revolutionary approach. He is the CEO of Pharnext, a company that’s using machine learning applications in healthcare to analyze the effects of current medications and use them, in combination, to create new treatments. He discussed the idea with Fortune Magazine, in which he boldly claimed, “with 50 drugs, we can treat everything”.

The idea of using multiple drugs to treat diseases isn’t new. Combinations of drugs are used during chemotherapy and in the treatment of HIV and AIDs, but by drafting machine learning in to help, Pharnext could create more of these combinations and at a faster rate than ever before.

If this approach is right, then the cure for cancer could already be among these numerous combinations. Even if this approach is wrong, it’s clear that machine learning is the future and is making huge advancements in this industry.


As you can see, there are several practical machine learning implementations for healthcare. As you understand this list is incomplete, so if you want us to add some points or cover the existing information from another point of view – send us a message, and we will have a chat.

Another confusing thing for people researching the use of machine learning for  healthcare is a lack of understanding that machine learning often powers other software tools. If you want to learn more about that – we strongly recommend you reading other articles in our blog.

Machine learning and artificial intelligence are closer than you think. Read on how Azati developed a Semantic Search Engine for Bioinformatics Company based on Machine Learning.

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