What Is Machine Learning in Healthcare? Applications and Opportunities

Machine learning (ML) is a tool used in healthcare to help medical professionals care for patients and manage clinical data. It is an application of artificial intelligence, which involves programming computers to mimic how people think and learn. In healthcare, you can apply this to collect and manage patient data, identify healthcare trends, recommend treatments, and more. Hospital and healthcare companies have begun to recognise the ability of machine learning to improve decision-making and reduce risk in the medical field, which has led to several new and exciting career opportunities.

Machine learning in healthcare is an evolving field that is more accessible than people may realise. While “artificial intelligence” and “machine learning” might initially seem intimidating, many machine learning principles rely on fundamental mathematical and programming skills. Once you understand the basics behind machine learning, you can build these skills to address more advanced concepts and challenges. This can uncover new opportunities for innovation and diverse career paths in the healthcare space.

Rise of ML in healthcare settings

As technology expands, machine learning provides an exciting opportunity in healthcare to improve the accuracy of diagnoses, personalise healthcare, 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 healthcare providers may otherwise miss—all of this can directly impact your community's health.

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 and the Internet Of Medical Things (IoMT) in healthcare

When you use machine learning in healthcare, you rely on an ever-evolving patient data set. You can use this data to find patterns that allow medical professionals to recognise new diseases, make decisions about risks, and predict treatment outcomes. Because of the volume of patients and the diverse medical technologies used to collect data, having medical devices sync to a central “network” is a convenient way to compile large volumes of information.

The Internet of Medical Things (IoMT) 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. This allows for things like 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.

Types of AI relevant to healthcare

Machine learning falls under the broad category of artificial intelligence. While many types of artificial intelligence exist, certain ones are more applicable to the needs of the healthcare industry. Machine learning engineers in healthcare often focus on streamlining medical administrative systems (such as healthcare records), finding trends in large clinical data sets, and creating medical devices to assist physicians.

Within these focus areas, some of the most common types of artificial intelligence used are:

Machine learning—neural networks and deep learning

Neural networks, often called 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. In the healthcare field, ANNs can produce computer-generated outcomes similar to those that 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 healthcare field, you can use deep learning to analyse MRI and other medical images to detect abnormalities. This doesn't replace the doctor's role but 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 type of machine learning centred around the computer’s ability to understand, analyse, and generate human language. You use natural language processing to interface and communicate with the machine. One application of natural language processing in healthcare is pulling patient data from doctors' notes.

Physical robots

Physical robots are what they sound like—robots that are physically present in the room with a doctor. 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, freeing physicians' and medical administrators' time to devote to more valuable activities.

Applications of machine learning in healthcare

While new machine learning applications always emerge, the most common applications in healthcare are centred around improving the quality of care and patient health outcomes. Because of the broad uses of machine learning in healthcare, you may choose an area of specialisation. Understanding the different applications of machine learning in healthcare (like the ones listed below) can help you find the concentration that best suits your interests and career goals.

• Disease prediction: You can use machine learning to find trends, create connections, and make conclusions based on large datasets. This can include predicting disease outbreaks in communities and tracking habits leading to patient disease.

• Visualisation of biomedical data: You can use machine learning to create three-dimensional visualisations of biomedical data such as RNA sequences, protein structure, and genomic profiles.

• Improved diagnosis and disease identification: Identify previously unrecognisable symptom patterns and compare them with larger data sets to diagnose diseases earlier in development.

• More accurate health records: Keep patient records updated, accurate, and easy to transfer between medical centres, physicians, and medical staff.

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

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

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

• Developing 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 healthcare

While machine learning is an exciting frontier in healthcare, it comes with several ethical considerations. For one, transferring 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, which can provide stress and uncertainty during the diagnostic process. Patients may also rather hear negative healthcare news from a physician they trust than a machine.

In addition, 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 also have the potential to create biased algorithms accidentally, and predictions may be more or less accurate based on gender or race. As machine learning continues to integrate further into healthcare, governing bodies and clinicians must establish clear boundaries, protocols, and accountability early on to minimise later consequences.

How to learn machine learning for healthcare

To learn machine learning for healthcare, you can study how machine learning works and develop your computer systems and coding skills. A background in mathematics or computer science—or at least an affinity for the topics—can be helpful. Building your knowledge of medical procedures and terminology can also be beneficial.

Qualification options

Although finding a job working with machine learning in healthcare is possible, you may enjoy boosting your knowledge by earning a relevant qualification. A degree also can help you stand out from the competition when you apply for a job. Generally, you will need 2 to 3 A levels in relevant subjects to enter a degree programme. Consider a bachelor's or master's degree in one of the following areas:

• AI & machine learning

• Computer programming

• Computer science

• Data science

• Operational research

• Maths

• Physics

• Software engineering

• Psychology

• Statistics

Outside of earning a degree or qualification, completing an apprenticeship in data science, artificial intelligence, or digital and technology solutions can help you gain footing in the machine learning field and start building your skill set. To enter a degree apprenticeship in one of these areas, 4 to 5 GSCEs in grades 9 to 4 are typically required.

Skills

Most people who work in machine learning have strong computer programming and data manipulation skills. Some of the field's more commonly used software include R, Python, SQL, Power BI, and Excel.

In addition to coding in these languages, ML workers often understand the theory behind the algorithms used in programming and modelling. 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 that you be proficient with popular machine learning software, such as IBM Watson, Amazon, Google Cloud, and Microsoft Azure.

Certifications

While there are no formal certification requirements to be a machine learning professional, having a professional certificate may strengthen your application. When choosing a professional certification, look for one with proficiency and expertise in machine learning and artificial intelligence skills, such as knowledge of software packages, data modelling, machine learning algorithms, and statistics. Because these are highly sought-after skills by employers, showcasing your skills in relevant areas may help show why you are a strong candidate for the position.

Machine learning in healthcare career prospects, jobs, and salary

The demand for ML professionals in healthcare will likely rise over the next decades as doctors and healthcare facilities incorporate it into their practices. Considering your career prospects, you may find looking at the various field jobs and their average salaries helpful.

• AI engineer: £52,067 [1]

• Data scientist: £48,587 [2]

• Machine learning engineer: £54,668 [3]

• Machine learning scientist: £61,636 [4]

• AI data scientist: £48,446 [5]

Take the next step in your career.

Explore the exciting world of machine learning engineering in healthcare 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 determine if this is your career path. What’s more, the broad course offerings on Coursera allow you to find your niche and tailor your skill set to the career path that best fits you. Build your CV, your skill set, and your passion.