Machine Learning Unleashes New Healthcare Opportunities

March 4, 2021
Machine Learning Unleashes New Healthcare Opportunities

Nearly every conversation about technology these days involves machine learning. And medicine is one of the sectors that fully embrace artificial intelligence/machine learning (AI/ML) algorithms. Machine learning will be the driving force for innovation in healthcare, not to mention being the driving force for significant investments. Last year, the British government invested £20 million (nearly $28 million) in AI projects for medicine.

Machine learning investment is not limited to governments. The AI market in medicine is projected to exceed $18 billion by 2025. However, while more and more people see AI as a hot topic, the public still does not really understand the practical applications and impact of using machine learning to treat people.

Image by Proxet. Machine Learning in Healthcare
Machine Learning in Healthcare

Jeff Gorke, a former contributor at Forbes, shared his optimistic expectations about artificial intelligence. He said, “I believe AI will play an increasingly larger role in healthcare delivery; it’s a matter of time and necessity. The key is in the development, build, and parameters of the logic data scientists and SMEs (e.g., clinicians and healthcare executives) must communicate clearly.”

In this article, we will take a closer look at the application of artificial intelligence in medical science, how it enhances healthcare, and what practical value it holds.

Examples of Artificial Intelligence in Healthcare

Machine learning, or ML meaning medical, becomes more powerful every year. This technology has become an irreplaceable tool in many hospitals. Precision, higher success rates, improved quality of patients’ life, and affordability are only a few reasons for the broad adoption of AI in the healthcare sector.

“I’m convinced that the implementation of AI in medicine will be one of the things that change the way care is delivered going forward. It’s clear that clinicians don’t make as good decisions as they could. If they had support to make better decisions, they could do a better job.”

— David Bates, Chief of Internal Medicine at Harvard-affiliated Brigham and Women’s Hospital, Professor of Medicine at Harvard Medical School, and of Health Policy and Management at the Harvard T.H. Chan School of Public Health

Here are some of the examples of how machine learning will influence the medical sector.

Staying on Top of Wellbeing

In the last decade, the growth of the wellbeing industry has been dramatic. Major brands of electronics such as Apple and Samsung invest mind-boggling sums into popularizing wearable devices. AI has only been accelerating the industry’s growth. Smart wristbands and watches display notifications and allow users to control their phones, and most importantly, they gather vital information on users’ activity, sleep, heartbeat, level of stress, etc. AI algorithms analyze this data to reveal patterns, identify reasons for medical concerns and provide powerful insights into improving users’ lifestyles.

Data-driven Clinical Judgment

Deep learning in healthcare takes the guesswork out of the diagnosis, especially in treating dangerous conditions such as cancer and retinopathy. For instance, by running an AI-powered test on mammograms, doctors receive results 30 times faster. It is one of the most reliable ways to diagnose patients, with 99% accuracy. Software also provides medical workers with findings and a detailed explanation of what factors lie behind the conclusions. It increases doctors’ competence and indicates the most suitable treatment.

Developing Medicine

It may come as a surprise to end consumers, but developing, testing, and approving medicine costs pharmaceutical companies billions. The reason? The process is lengthy and expensive, and the percentage of drugs that become approved for the general market is a tiny fracture of the medicine in development. AI algorithms enable companies to significantly reduce this process's cost by substituting the trial and error approach with testing hypotheses based on data. Another powerful capability of machine learning is that it allows researchers to identify repurposing opportunities. By running complicated tests automatically, machine learning can determine if the drug can be modified to treat other diseases.

Application of Machine Learning to Medical Diagnosis

There are four main ways of implementing AI solutions to help medical staff and patients benefit from its capabilities at the stage of diagnosing:

  • Chatbots. These are robot-driven chats. They are trained to recognize text and speech, understand patients’ complaints, and process them to reveal data-based patterns. With this information, chatbots can provide timely diagnoses, give recommendations, and direct users to the right specialists.
  • Rare diseases and disorders. Doctors resort to machine learning diagnosis to detect uncommon illnesses with only patients’ photos. Face recognition and artificial intelligence can accurately identify genetic conditions. It drastically cuts down on diagnosing time and enables doctors to provide treatment to improve patients’ quality of life.
  • Clinical tests. Machine learning increases the speed and accuracy of analyses of blood, urine, tissues, etc. By comparing the samples' results with existing databases, special medical software provides comprehensive reports that are inaccessible using traditional methods. Technology also significantly decreases the likelihood of human error, further reducing costs to and impact on human health and, in some cases, even lives.
“We've seen artificial intelligence unleash compelling insights for making more informed decisions across industries. As for healthcare, the technology helps identify diseases fast and accurately and, therefore, improves patient outcomes. That's why we believe that doctors and medicine generally will greatly benefit from AI-driven solutions.”

Vlad Medvedovsky at Proxet, a custom software development solutions company

Data Science for Actionable Insights

In the information age, data is the king and reigns over all spheres of our lives. But it’s not always easy to put to use. Gathering information is only half the battle. The real challenge is to process it in a way that reveals accurate and precise insights.  That is why machine learning is an indispensable tool for high-stakes, high-impact healthcare data science projects.

Here is how artificial intelligence enhances data science to help medical workers derive actionable insights:

Tracking Health to Prevent Complications

As we’ve already mentioned, wearables have become a significant part of the healthcare system. One of the favorable results of this trend is that people don’t need to go to hospitals anymore to identify some problems. Having processed user data, machine learning can point out any abnormalities and even diagnose heart and respiratory illnesses.

Also, doctors strongly recommend wearables for people who suffer from chronic diseases. In some cases, mass market devices do the trick. However, to receive better help, patients need gadgets developed specifically for their conditions, such as diabetes and hypertension. Instead of gathering general information, these devices analyze data relevant to the disease to avert a potential crisis.

Taking Care with Precision

Data science helps gather the information to diagnose a case or monitor chronic disease and gives deep insights into a patient's recovery. This knowledge helps establish personalized medicine. Well-informed patient-centered care decreases the mortality rate and makes patient outcomes more predictable.

It will end the era where treatment was issued based on general recommendations for a disease. Based on Electronic Medical Records combined with genetic information, the doctors can give care based on the patient’s condition, needs, and progress.

Data for Hospital Management

Data science-driven insights for business development have been present in many industries for years, and hospital operations management is no exception. Deep learning empowers businesses with essential data about the required services in the area, what works well and what needs to be improved, and the opportunities to grow businesses. After receiving these insights and acting on them, hospitals improve their performance and engagement with patients.

It creates a new concept in the IT market: insight as a service. Clients receive the software that helps access all the available data, check its credibility, view medical reports examples, and perform analysis. With it, everyone can make better decisions regardless of their position and work duties.

Modern Medicine Examples

Modern medicine has dramatically progressed. Many perplexing devices and tools we read about in sci-fi books have become a day-to-day experience in many medical institutions. Here are some of the examples of the advancement of modern healthcare:

  1. Wearable patches
    These are special patches used to track the patient’s wellbeing while staying in the hospital. Their main advantage over traditional cables is that they read and analyze information with AI continuously rather than periodically. Also, it doesn’t restrict patients’ movement and level of comfort.
  2. Monitoring from the within
    To track the efficiency of prescribed medicine, patients take a small chip together with their pills. The chip travels through the digestive system with the medication to monitor the efficacy of the medication and identify potential side effects. The chip uses gastric juice as a power supply and transmits the findings to machine learning smart health records, which are continuously updated.
  3. Robo-surgeons
    Robotic surgeries have been practiced for decades now, but nanobots take operations to a new level. This kind of surgery is less harmful and doesn’t provoke inflammation. However, the main advantage of such small robots is the precision with which they operate, making the alterations solely in areas that need it without hurting other tissues. And the hyperfast connection provided with 5G technology combined with AI will empower surgeons to conduct surgeries remotely.
  4. AR and VR
    Augmented and virtual reality do not directly treat patients but contribute significantly to the advancement of medicine. The primary purpose of this technology is to educate. With AR’s and VR's help, medical students have an invaluable opportunity to gain practical experience without risking patients’ lives. The technology provides complete engagement: students learn by doing, involving visual, audio, and kinesthetic senses. AI helps to create an even more immersive experience.

Proxet has a renowned expertise in implementing machine learning solutions in the healthcare sector. It helps businesses identify the market's needs, offers tailored solutions to meet them, and provides full-cycle development. Our experienced product owners and developers will gladly assist you in visualizing and creating your product.

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