Technologists can now create computers that are capable of learning without the need for human intervention. These machine learning tools are incredibly useful and most industries have developed business processes that leverage them. The healthcare industry is no exception as machine learning is becoming more widely used to help patients and clinicians in a variety of ways.

In many instances, machine learning technology (ML technology) is used to enhance the decision-making processes for healthcare providers. For example, deep learning technology can be used to identify complex patterns in certain data sets, like conventional radiographs, CT images, MRI images, or radiology reports. Additionally, using machine learning to supplement diagnostic procedures has had positive results. In fact, machine learning-based automatic detection and diagnostic systems have been shown to be as effective as experienced radiologists.

It’s becoming more apparent that ML technology is shaping the future of the healthcare industry which is why it’s important to learn and understand it.

ML Technology Defined

Machine learning is a data analysis methodology that automates analytical model building. The main use of machine learning is to create systems that can identify patterns and make decisions with minimal human intervention. This process has evolved significantly over time, and cutting-edge advancements are being made regularly.

Machine learning programs allow systems to learn from data and improve from experience without being explicitly programmed. By using machine learning-supported healthcare analytics tools, healthcare organizations can provide better information to doctors and other healthcare providers at the point of patient care.

So far, machine learning technologies have replaced a number of redundant manual processes in medicine, paving the way for a more efficient, AI-powered healthcare system.
The Evolution
In the late 1900s, data-driven machine learning (ML) tools were introduced to replace the knowledge-driven programs of the past. These modern tools could analyze large amounts of data to draw accurate conclusions from the results. In doing so, the systems are effectively “learning”, hence why they are called machine learning tools.

These programs are relevant in nearly all industries, ranging from financial services and government to healthcare, retail, and transportation. Research into ML technology is rapidly expanding because the incentive is so great. You can see why, as modern machine learning programs are capable of incredible things, such as guiding self-driving cars and recommending products in real-time.

Machine Learning in Healthcare

Research into using machine learning tools for medicine began in the 1960s, when the first problem-solving program, Dendral, was created. This program was the basis for MYCIN, one of the most significant early uses of AI in the medical field. Although these tools were innovative breakthroughs at the time, they did not achieve routine use by healthcare practitioners. 

The next few decades held significant advancements in the development of microcomputers and networking tools, creating the perfect conditions for new ML technology to be born. At this time, researchers and scientists recognized that in order for machine learning tools to be useful in healthcare, the programs must be designed to build on the expertise of physicians while accommodating the absence of perfect data. With these criteria in mind, the industry has moved forward significantly.  

Where It Stands Now

Although it took quite some time, machine learning tools have become prevalent in the healthcare industry and are frequently used to improve and optimize the delivery of services. Initially, machine learning technologies were most commonly used for identifying diseases and performing diagnostic procedures, but, over time, machine learning also became useful for automated medical billing, outbreak prediction, clinical trial research, and behavioral modification. 

ML technology is also widely used for recording, storing, and analyzing smart health records. Through this mass collection and processing of data, medical scientists and other healthcare professionals are able to discover new scientific findings that enable them to better serve their patients. 

Benefits 

At a high-level, machine learning tools offer objective information that can be used to improve the efficiency, reliability, and effectiveness of the healthcare systems. Specialized programs can be designed within these tools to achieve a range of different outcomes, such as predicting population health risk patterns and analyzing laboratory results to develop new drugs at lower costs. More specifically, the benefits to the healthcare industry include: 

  • Seamless patient management and data processing
  • Connect patients with doctors that better match their specific needs
  • More accurate diagnosis
  • Faster drug development 
  • More efficient research
  • More easily manage chronic illnesses

Use Cases

There is a vast market for health products that provide researchers and clinicians with detailed data that can be used to treat patients and inform future advancements in the healthcare field. For example, at MD Anderson, researchers have developed a medical machine learning algorithm that can predict acute toxicities in patients receiving radiation therapy for head and neck cancers. Using this deep learning tool, the researchers were able to identify complex patterns that could be examined to find medically helpful insights. 

The Future of Machine Learning Technology

Traditionally, physicians must examine patients, study their symptoms, and use their knowledge to diagnose the illness and strategize an effective approach to treatment. With the aid of ML technology, less pressure is on the physician to be the one to come up with answers. Machines crunch the numbers in their place. 

Through computational analysis of immense volumes of data, they reach accurate conclusions which unearth valuable insights that can prove essential in the process of treating and diagnosing patients. As the capabilities of ML technology continues to advance rapidly, we are sure to see analytics platforms that do a lot of the behind-the-scenes work by delivering critical information to healthcare providers in real-time.

As this type of technology becomes more widely available, healthcare organizations will need supplementary technology that is customized for the specific needs of their practice. That’s where illumisoft comes in. 

If you are looking to upgrade the technology in your organization, illumisoft can help you develop a custom software application to be used by your patients and staff. This will effectively help to increase revenue and optimize your delivery of health services.

 

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