Machine Learning in Healthcare

Machine Learning in Healthcare: How ML Can Help Medicine

In today’s world, every industry is becoming digitalized. This includes the healthcare sector. As a result, the capability to capture, inspect, interpret, share, and distribute data has become an urgent priority.

Machine learning helps technology powered by artificial intelligence (AI) address the challenges rampant digitalization poses to the healthcare industry. In particular, it can capture and interpret vast amounts of data, both incoming and outgoing.

Machine learning can help healthcare organizations lower costs through efficient operations, meet growing medical demands, and optimize patient comfort and satisfaction.

For patients, machine learning can be utilized by medical practitioners to help uncover and treat diseases more effectively with personalized care and pinpoint accuracy. Fortunately, it is being used extensively throughout the healthcare industry.

This guide will point out the correlation between machine learning and healthcare and detail how the innovation can benefit the latter.

What is Machine Learning?

What is Machine Learning

Machine learning is one of the most fundamental aspects of AIMachine learning is the innate ability of technology to capture and interpret patterns of data to aid in decision making.

To put it simply, machine learning is how technology and innovations process and learn new information. Applications that utilize machine learning consist of complex mathematical and scientific algorithms, basically an in-depth collection of processes to complete specific tasks.

These algorithms are designed to rhythmically interpret data independently without human interaction. Nowadays, machine learning algorithms have improved their accuracy to the extent where human-led programming is no longer necessary.

The basis of machine learning is built on three primary categories:

  • Representation – This entails the technology being classified into a language that the computer can handle and process.
  • Evaluation – This procedure determines whether or not the data classifications are beneficial or can be interpreted.
  • Optimization – Finally, the algorithms determine the ideal model for achieving the most precise outputs.

The most recognizable example of machine learning can be found in what people use every day: Google. The popular search engine has built an enormous reputation for being the world’s most trusted resource.

In fact, Google is even synonymous with researching something. One may wonder how Google is able to process trillions of websites and rank them accordingly based on search terms or keywords that people use to find information, shop online, and locate businesses.

The answer is that Google relies on using algorithms based on AI and machine learning. Essentially, Google has a unified algorithm that determines the search intent of every possible keyword.

For example, machine learning is used to differentiate whether or not a person wants to buy something or find more information when they insert the keyword “teddy bear” into the search engine bar.

Finally, machine learning is integral in producing accurate search results based on what internet users are looking for. Google’s algorithms learn from human interaction every day and do so independently.

This is the basis of how machine learning works, but it’s far from the only way it’s used, especially in healthcare.

What are the Types of Machine Learning?

Again, machine learning makes it possible for technology to learn. Machine learning used foundational models to solve problems. The algorithm will change depending on the data it receives and result in the machine discovering new patterns.

As a result, machine learning is how machines adapt, and it leads to highly precise outputs. Algorithms are typically constructed without human-led programming. Below, you’ll find the different types of machine learning used in various applications:

  • Supervised Learning – This type of machine learning takes place when data is fed to machine learning algorithms to produce accurate outputs. This will continue to be the most used method of machine learning until at least 2022.
  • Unsupervised Learning – Algorithms can easily identify patterns and interpret data without prior classifications. Unsupervised machine learning is used in various applications. It can be used to discover faults in factory systems to reduce repair and replacement costs through predictive maintenance.
  • Semi-supervised Learning – This type of machine learning falls between both supervised and unsupervised learning. Semi-supervised machine learning algorithms can use both classified and unclassified data to troubleshoot models. This model of machine learning can be used to accelerate drug discovery innovations.
  • Reinforcement Learning – Reinforcement machine learning teaches technology through what is known as a rewards system. These algorithms produce several outputs and are rewarded when they make an accurate selection. Much like how reinforcement learning is used to train pets, livestock, and service animals, it can also be implemented in machine learning.

How is Machine Learning Used in the Healthcare Industry?

Machine learning can improve the precision of treatment procedures and medical outcomes by using savvy algorithms. For example, deep learning is a type of machine learning that replicates human brain activity.

It’s being increasingly used in radiology. In cancer treatment protocols, neural networks can learn from input data without any human supervision. Deep learning applications can even analyze, recognize, and detect cancerous growths from images.

What’s more is that cloud infrastructures and accelerated processing speeds invigorate machine learning algorithms, making them more capable of observing finer details and anomalies in medical images beyond the human eye and resulting in early detection and management of diseases.

Future machine learning innovations in healthcare will continue to move forward and transform how medical practitioners operate. For now, machine learning can be used to diagnose diabetic retinopathy quicker than a doctor can and use predictive analytics to analyze breast cancer relapse based on images and even a patient’s medical records.

How Does Machine Learning Improve Healthcare Informatics?

Machine Learning Improves Healthcare Informatics

It has already been stated how machine learning can impact the efficacy of medical practitioners. The innovation also plays an integral role in healthcare informatics:


Healthcare organizations process millions of patients every year. Keeping accurate records is essential for both state and federal compliance mandates. Machine learning can be applied in healthcare informatics, especially when it comes to electronic health records (EHRs).

Artificial intelligence helps to reduce administrative and healthcare costs, improve patient care, and streamline organizational operations. Take, for example, natural language processing, which allows healthcare professionals to automatically record clinical notes and bypass manual entry.

Machine learning algorithms can even boost EHR management applications to provide accurate decision support to medical professionals via telepathic solutions and image analysis.

Data Integrity:

Unfortunately, if there are any gaps in healthcare information inputs, inaccurate outputs can be generated even with machine learning. This can negatively influence how decisions are made by doctors and other healthcare providers.

For this reason, it’s highly important to ensure that accurate EHR data is prepared before it can be effectively usedMachine learning can be used to maintain the integrity of EHR data before it’s inputted.

This can be achieved with a far lesser margin of error compared to humans.

Predictive Analysis:

Predictive analysis, combined with health informatics and machine learning, can improve patient outcomes, elevate clinical decision-making, and heighten core healthcare processes.

The potential of machine learning does not only help in interpreting healthcare data. This innovation is also instrumental in predicting outcomes much faster and more effectively than even the most experienced doctors.

In sum, machine learning can impact the healthcare industry in the following ways:

  • Electronic Health Records (EHRs)
  • Disease Identification and Diagnosis
  • Automatic Patient Support Tasks
  • Robotic Surgery
  • Medical Imaging Diagnosis
  • Personalized Medicine
  • Robotic Process Automation (RPA) 

Below, you’ll learn how machine learning influences a few of these in detail.

What are Other Applications of Machine Learning in Healthcare?

Machine learning can improve patient care delivery. It mainly allows doctors to identify and diagnose new diseases. As you will find out below, machine learning can be used in other ways:

Disease Identification and Diagnosis:

Machine learning algorithms can accurately observe patterns in patient data, either through images or patient records. Recent news has indicated that machine learning has increased access to healthcare in third-world countries while providing further innovation in cancer identification and treatment.

For example, it has been reported that deep learning algorithmic models can be used to detect the occurrence of breast cancer in patients several years in advance. In fact, machine learning can detect the outcome of skin cancer faster and more accurately than the most experienced dermatologists.

Medical Imaging Diagnosis:

MRIs and CAT scans are commonly used in the healthcare industry at the most basic level. Yet, these scans delve into highly specific resolutions that can trouble experienced pathologists and radiologists.

Machine learning has proven its ability to help healthcare professionals improve their accuracy and productivity. In particular, medical imaging diagnosis, backed by machine learning, can help doctors find abnormalities in scans they wouldn’t be able to naturally see.

Robotic Surgery:

Machine learning can use real-time data from previous surgeries and patient records to achieve pinpoint precision when surgical robotic tools are utilized. The benefits include less invasive surgeries, machine aid, and minimized human error.

For example, robotics can help doctors in high-stress situations, usually in spine surgeries and during blood vessel unclogging procedures. Machine learning can also aid in planning executions and workflows that are necessary in surgical procedures.

Robotic Patient Support Tasks:

Robots, empowered by machine learning, can improve patient rehabilitation. For example, these robots can help paralyzed patients regain their ability to walk over time.

Robots can also provide reminders to patients to take their medications and check their blood pressure, in addition to offering companionship to older and sick people.

Personalized Medicine:

Genetic data and EHRs can help improve personalized patient care. In reality, healthcare professionals don’t have the time to assess all of their medical data to provide the best-personalized care to patients.

The good news is that machine learning can help. It can leverage predictive analysis and big data to make more accurate judgments and help doctors provide personalized care for the most specific medical conditions.

Are Machine Learning Applications Ethical?

Are Machine Learning Applications Ethical

The way machine learning is used in healthcare does result in higher efficiency in diagnosis, treatment, and personalized care. Though, there are some ethical concerns. The digitization of the healthcare industry has not done anything to quell the concerns of machine learning meeting standard ethical conditions.

These ethical concerns include:

Sharing Patient Information:

The confidentiality of patient data is a huge concern in the healthcare industry. Federal laws are clear that the patient must provide consent to distribute, disseminate, and share patient information.

Machine learning relies on data, whether humans supervise its integration or not. Many wonder whether or not gaps in security can still lead to a data breach and compliance issues.

Laws also prevent doctors from sharing a patient’s medical records with other doctors. How machine learning can exacerbate this issue remains to be seen.

Patient Autonomy:

Ultimately, people hold the ultimate discretion in how their medical treatment will play out. Doctors traditionally hold some sway, but machine learning can help them make more accurate solutions to help the patient.

As a result, some issues can arise when patients do not agree with the logical data machine learning produces. Doctors will then face an ethical dilemma in trusting their medical data or fulfilling the patient’s wishes.

Patient Safety:

Machine learning has a far less margin of error compared to humans. However, many people still remain uneasy if a machine were to determine their fate. This creates another dilemma, especially since machine learning relies on data inputs that could be incorrect.

Flawed health data can negate the power of machine learning and thwart surgical procedures, diagnoses, and care. These errors, while uncommon, can lead to legal issues if they arise.

Curious About Machine Learning in Healthcare?

The healthcare industry is responsible for saving billions of lives across the world. For this reason, medical practitioners are trusted with the most advanced technology that’s powered by machine learning.

If you’re curious about how this technology can be used in your organization and don’t know where to start, you’ve come to the right place. At, we pride ourselves on being one of the most qualified medtech developers in the country.

Contact us today to speak to a member of our team about learning more about potential machine learning applications in your organization.

Ryan is the VP of Operations for He brings over a decade of experience in managing custom website and software development projects for clients small and large, managing internal and external teams on meeting and exceeding client expectations--delivering projects on-time and within budget requirements. Ryan is based in El Paso, Texas.
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Ryan Nead