Machine Learning in Healthcare: 9 Future Trends to Watch

Machine learning is making huge strides in the healthcare sector

The potential of machine learning for healthcare

Machine learning is a rapidly rising technology with exciting implications for healthcare. Already it's helping tackle some of the most difficult issues in the space, from making sense of huge volumes of patient data to improving the quality and personalisation of treatment and care. So what's machine learning and how might it elevate healthcare in the coming years? We explore how it's already transforming the sector and its potential.

What is machine learning?

Machine learning is one type of technology within the cluster of technologies known as artificial intelligence. According to one definition of machine learning, it's a statistical technique for applying models to data and having AI learn by training these models with data. Additionally, machine learning also refers to systems, apps, or programs being able to identify patterns within massive volumes of data to make predictions. Alternatively, another way to define machine learning is to conceptualise it as developing algorithms and apps based on past experiences and current data – both historical and real-time data.

It's not only the healthcare sector that's benefiting from the technology. For example, the agricultural, manufacturing, hospitality, retail, and banking sectors are also relying on data science tools including machine learning. What's more, even nonprofit projects like humanitarian aid can use machine learning.

9 machine learning trends in healthcare

These are some of the biggest machine learning trends in healthcare to be aware of:

1. Precision medicine and personalisation of healthcare – Machine learning is already widely used for precision medicine. It predicts successful treatment protocols using patient data and the treatment context. Precision medicine enables highly specific, personalised treatment plans and can lead to better clinical outcomes.

2. Categorisation applications – Categorisation applications include processes like working out whether or how likely a patient will develop a certain condition. This can be used to inform policy, and effective prevention measures, and help providers plan for capacity.

3. Analysing imaging – Machine learning is already used to analyse radiology and pathology images. In addition, it's used to classify high volumes of images quickly. In the coming years, the use of machine learning for these processes could become even more sophisticated and accurate.

4. Claims and payment administration – Incorrect claims can cost insurers, governments, and providers a considerable amount of time, money, and effort. Machine learning can streamline claims and payment administration by, for example, facilitating more accurate claims data and ensuring claims are correct.

5. Other administrative processes – Machine learning can be used in a vast array of administrative processes, including claims processing, clinical documentation, revenue cycle management, and medical data management. It can even be used to develop patient-facing tools, such as chatbots for telehealth, mental health and wellness support, and other general interactions not requiring doctors' input.

6. Prediction and health policy – Machine learning offers immense potential for predictive modelling and health policy. For example, population health machine learning models can be used to predict which populations are at risk of certain accidents or conditions and even hospital readmissions. Similarly, tapping into data on social determinants of health and using machine learning to identify trends can inform policy. Governments and organisations could better target patients at higher risk of preventable conditions like heart disease and diabetes.

7. Electronic health records – Machine learning can help make sense of the vast quantities of data now available through electronic health records (EHR). Most of these are in the form of free-form text entries, which are also known as unstructured data. Machine learning has the potential to interpret this free-form data rapidly to glean valuable insights at scale, for millions of patients, to empower better decision-making throughout the whole patient-care cycle.

8. Diagnosis and treatment – Machine learning is increasingly being used for diagnosis and treatment recommendations. Clinical decision support tools (CDS), in particular, can leverage machine learning to enhance the healthcare provider's decision processes for the best possible care. CDS tools analyse huge volumes of data to inform treatment suggestions. They can also flag likely problems so providers can take preventative measures.

9. Drug development – Researchers rely on machine learning to put together cohorts for expensive clinical trials, paving the way for better studies and faster, more effective drug development. As such, researchers can make data-driven decisions and more easily identify key patterns and trends, and consequently, achieve greater efficiency in their studies.

Machine learning and healthcare in the coming years

Machine learning is already starting to fulfill its potential for healthcare, from facilitating more effective drug research and development to patient care and administrative processes. In the coming years, widespread adoption of machine learning and other AI technologies is likely. Rather than completely replacing clinicians, these technologies are likely to complement and enhance their roles. Long-term outcomes could include better quality of care and a more efficient and cost-effective healthcare system, which can only benefit patients, providers, insurers, regulators, and policymakers.