What Is Machine Learning in Health Care?

Written by Coursera Staff • Updated on

Learn more about machine learning in healthcare. Find out how artificial intelligence can improve health care and what exciting careers are available in this field.

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Within the health care industry, tools and technologies have consistently evolved throughout the years. Today, machine learning (ML) has been key to advancing care and streamlining data for patients. Medical professionals can now collect and manage patient data, identify health care trends, and recommend treatments with the help of machine learning.

Machine learning can help health care providers improve decision-making and reduce risks in the medical field. The industry's use of machine learning is just beginning, and since health care is a stable field, there are plenty of opportunities for a career path in machine learning that is focused on the health care space.

We'll provide some insight into what machine learning in health care entails, so you can decide whether it is the right career path for you.

What is machine learning in health care?

Machine learning 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 datasets 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.

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 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 onto the market, the IoMT is predicted to expand exponentially.

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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.

How ML is used 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 brain. 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 a machine learning type 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.

Real-world examples of 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 find trends, create connections, and make conclusions based on large datasets. 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 structure, 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 the area where they work, 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 the 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 the use of 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 a biased algorithms, which can lead to unnecessary discrimination. As the field of machine learning continues to integrate 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: $112,730 [1]

  • Data scientist: $127,385 [2]

  • Health care technology consultant: $118,047 [3]

  • Machine learning engineer: $123,143 [4]

  • Machine learning scientist: $138,863 [5]

  • Pharmaceutical commercial data analyst: $72,518 [6]

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 also can 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 & 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 approaches, unsupervised learning approaches, reinforcement learning approaches, and deep learning.

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 you have proficiency with popular machine learning software, such as IBM Watson, Amazon, Google Cloud, and Microsoft Azure.

Read more: Machine Learning Skills: Your Guide to Getting Started

3. Earn a certification.

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

Read more: 5 Popular Machine Learning Certifications: Your 2023 Guide

Take the next step in your career

Explore the exciting world of machine learning engineering in health care through courses offered by the world’s top universities on Coursera. Online courses like Fundamentals of Machine Learning for Healthcare or AI in Healthcare, offered by Stanford University, can help you determine if this is the right career path.

Article sources

1

Glassdoor. "Artificial Intelligence Engineer Salaries, https://www.glassdoor.com/Salaries/artificial-intelligence-engineer-salary-SRCH_KO0,32.htm." Accessed October 25, 2023.

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