What Is Machine Learning in Health Care?

Key takeaways

Machine learning can help health care providers improve decision-making and reduce risks in the medical field. 

• Machine learning in health care works by sorting the vast amounts of data from medical devices and records and using it to uncover trends and patterns.

• Machine learning has applications in various aspects of health care, like disease prediction, biomedical data visualization, assisted surgery, and personalized treatment.

• You can use various machine learning technologies in health care, including neural networks, natural language processing, and robotic process automation.

Explore what machine learning in health care entails so you can decide whether it is the right career path for you. Afterward, if you’re ready to start learning more about machine learning algorithms, enroll in the Deep Learning Specialization. You’ll have the opportunity to gain experience with building various types of neural networks for different applications in as little as three months. Upon completion, you’ll have earned a career certificate for your resume.

How is machine learning used in health care?

Machine learning (ML) in health care relies on the collection of patient data. Using systems and tools designed to sort and categorize data, machine learning algorithms can discover patterns in data sets that allow medical professionals to identify new diseases and predict treatment outcomes.

The volume of data collected from patients in a health care facility, let alone in a state or country, is vast. The only way to sync it is by ensuring all medical devices and records are part of a central network that allows data scientists to find trends and patterns.

Read more: Big Data in Health Care: What It Is, Benefits, and Jobs

The Internet of Medical Things (IoMT)

You may have heard of the Internet of Things (IoT). Did you know there is also the Internet of Medical Things (IoMT)? This is the network of medical devices and applications that can communicate with one another through online networks. Many medical devices are now equipped with Wi-Fi, allowing them to communicate with devices on the same network or with other machines through cloud platforms.

The IoMT allows for remote patient monitoring, tracking medical histories, tracking information from wearable devices, and more. As more wearable and internet-equipped medical devices come into the market, the IoMT is predicted to expand exponentially.

The rise of machine learning in health care

As technology expands, machine learning provides an exciting opportunity in health care to improve the accuracy of diagnoses, personalize health care, and find novel solutions to decades-old problems. You can use machine learning to program computers to make connections and predictions and discover critical insights from large amounts of data that health care providers may otherwise miss. All of this can add up to a direct impact on the health of your community.

The goal of machine learning is to improve patient outcomes and produce medical insights that were previously unavailable. It provides a way to validate doctors’ reasoning and decisions through predictive algorithms. For example, suppose a doctor prescribes a specific medication for a patient. In that case, machine learning can validate this treatment plan by finding a patient with a similar medical history who benefitted from the same treatment.

Machine learning applications in health care

Machine learning engineers in health care often focus on streamlining medical administrative systems (such as health care records), finding trends in large clinical data sets, and creating medical devices to assist physicians. Here are some ways machine learning is used in health care.

Neural networks and deep learning

Neural networks, often referred to as artificial neural networks (ANNs) or simulated neural networks (SNNs), are a subset of machine learning that imitates the structure of the neural networks in our brains. You can use ANNs in the health care field to produce computer-generated outcomes similar to what human reasoning would lead to when making a diagnosis.

ANNs are the basis of deep learning, which is the ability of the ANN to learn from large amounts of data. In the health care field, you can use deep learning to analyze MRI and other medical images to detect abnormalities. This doesn't replace the doctor's role, but it enhances the doctor's work by speeding up the time it takes to form a diagnosis and start patient treatment sooner.

Natural language processing

Natural language processing is machine learning centered around the computer’s ability to understand, analyze, and generate human language. You use natural language processing to interface and communicate with the machine. One application of natural language processing in health care is pulling patient data from doctors' notes.

Robots

Robots can support surgeons during complex procedures that require precise movements. In many cases, robotic surgery reduces the procedure's invasiveness, which can also lower complications and improve outcomes.

Robotic process automation

Robotic process automation is a type of machine learning that mimics human actions for manual tasks such as data entry. Medical companies and hospitals use machine learning to automate these tasks. This can free up the time of physicians and medical administrators to devote their efforts to more valuable activities.

Examples of AI and machine learning in health care

The most common applications in health care are centered around improving the quality of care and patient health outcomes. Understanding the different applications of machine learning in health care can help you find the concentration that best suits your career goals. Check out these real-world examples:

• Disease prediction: Machine learning can be used to find trends, create connections, and make conclusions based on large data sets. Data engineers can use information to prevent disease outbreaks in communities and track habits that lead to disease.

• Biomedical data visualizations: You can use machine learning to create 3-D visualizations of biomedical data such as RNA sequences, protein structures, and genomic profiles.

• Improve diagnoses: Identify previously unrecognizable symptom patterns and compare them with larger data sets to diagnose diseases earlier in their development.

• Accurate health records: Keep patient records updated, accurate, and easy to transfer between clinics, physicians, and medical staff by employing machine learning to filter out errors and blanks.

• AI-assisted surgery: Support surgeons by performing complex tasks during surgery, giving surgeons a better view of their work area, and modeling how to complete procedures.

• Personalized treatment options: You can use machine learning to analyze multi-modal data and make patient-tailored decisions based on possible treatment options.

• Medical research and clinical trial improvement: You can use machine learning to enhance the selection of participants for clinical trials, data collection procedures, and analysis of results.

• Develop medications: You can use machine learning to identify potential pathways for new medicines and develop innovative drugs to treat varying medical conditions.

Ethics of machine learning in health care

While machine learning is an exciting frontier in health care, it comes with several ethical considerations. For one, the transfer of medical decision-making from solely human-based to smart machines raises questions about privacy, transparency, and reliability. Patients cannot discuss their care with machines as they can with a physician, nor would they want to speak to a robot during what could be a stressful experience.

Mistakes in patient diagnosis are likely unavoidable, and medical facilities may try to avoid accountability for who is responsible for an inaccurate AI-assisted diagnosis. Machine learning engineers might make a mistake and accidentally create biased algorithms, which can lead to unnecessary discrimination. As machine learning continues to integrate into health care, governing bodies and clinicians must establish clear boundaries, protocols, and accountability early on to minimize later consequences.

Career paths and salaries

The demand for ML professionals in health care will likely rise over the next decades as doctors and health care facilities incorporate it into their practices. As you consider your career prospects, you may find it helpful to look at the various jobs available in the field along with their annual salaries.

• AI engineer: $148,000 [1]

• Data scientist: $153,000 [2]

• Health care technology consultant: $173,000 [3]

• Machine learning engineer: $158,000 [4]

• Machine learning scientist: $204,000 [5]

• Pharmaceutical commercial data analyst: $105,000 [6]

All salary information represents the median total pay from Glassdoor as of November 2025. These figures include base salary and additional pay, which may represent profit-sharing, commissions, bonuses, or other compensation.

How to get into machine learning in health care

To learn machine learning for health care, you can study how machine learning works and develop your computer systems and coding skills. A background in mathematics or computer science can be helpful. These steps can get you closer to your goals.

1. Consider degree options.

Although finding a job working with machine learning in health care is possible, your chances are greatly improved with at least a bachelor's degree. A degree can also help you stand out from the competition when you apply for a job. Consider a bachelor's or master's degree in one of the following majors:

• AI and machine learning

• Computer programming

• Computer science

• Data science

• Information technology

• Mathematics

• Machine learning

• Physics

• Software engineering

• Statistics

2. Boost your skills.

Most people who work in machine learning have strong computer programming skills. Some of the field's more commonly used coding languages include C, C++, Java, Julia, Python, R, Java, and Scala.

In addition to coding in these languages, ML workers often understand the theory behind the algorithms used in programming and modeling. This includes algorithms across supervised learning, unsupervised learning, reinforcement learning, and deep learning approaches.

Depending on the exact nature of the job, the emphasis and requirements will vary. Often, you will use a mix of computer program foundations, software engineering and design, data science, and machine learning skills. Employers may also recommend that you have proficiency with popular machine learning software, such as IBM Watson, Amazon, Google Cloud, and Microsoft Azure.

3. Earn a certification.

While there are no formal certification requirements to be a machine learning professional, having a certification in the area may strengthen your application. Specializations and Professional Certificates like Mathematics for Machine Learning from Imperial College London or the IBM Machine Learning Professional Certificate can help you build your knowledge and skills.

Discover practical guides for your career journey

Ready to sharpen your skills? Keep up with the latest trends and in-demand skills in your industry by subscribing to our LinkedIn newsletter, Career Chat! Or if you want to keep learning more about the field of machine learning, check out these resources:

• Take the quiz: Machine Learning Career Quiz: Is It Right for You? 

• Watch on YouTube: Why I Waited to Learn Machine Learning (& Why I Shouldn't Have) 

• Bookmark for later: Artificial Intelligence Glossary: Learn AI Vocabulary 

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