HomeMachine LearningMachine Learning NewsMachine Learning in Healthcare: 9 Future Trends

Machine Learning in Healthcare: 9 Future Trends

Machine learning is making significant progress in the healthcare sector.

Machine learning’s potential in healthcare

Machine learning is a rapidly developing technology that has exciting implications for healthcare. Already, it is assisting in the resolution of some of the most difficult issues in the field, such as making sense of massive amounts of patient data and enhancing the quality and personalization of treatment and care. So, what exactly is machine learning, and how will it impact healthcare in the coming years? We look at how it is already transforming the industry and its potential.

What is machine learning?

Machine learning is a subgroup of AI technology. Machine learning, according to one definition, is a statistical technique for applying models to data and allowing AI to learn by training these models with data. Furthermore, machine learning refers to systems, apps, or programs that can identify patterns in massive amounts of data to predict outcomes. Another way to think about machine learning is to imagine it as creating algorithms and apps based on previous experiences and current data – both historical and real-time data.

Technology benefits more than just the healthcare industry. The agricultural, manufacturing, hospitality, retail, and banking industries, for instance, rely on data science tools such as machine learning. Furthermore, machine learning can be used in non-profit projects such as humanitarian aid.

9 Healthcare Machine Learning Trends

The following are some of the most important machine learning trends in healthcare to be aware of:

  1. Precision medicine and healthcare personalization

Precision medicine already makes extensive use of machine learning. Using patient data and the treatment context predicts successful treatment protocols. Precision medicine allows for highly specific, individualized treatment plans, which can lead to improved clinical outcomes.

  1. Categorization applications

Categorization applications include processes such as determining whether or not a patient is likely to develop a specific condition. This can be used to inform policy and effective prevention measures, as well as to assist providers in capacity planning.

  1. Imaging analysis

Radiology and pathology images are already analyzed using machine learning. It’s also used to quickly classify large numbers of images. Machine learning for these processes may become more sophisticated and accurate in the coming years.

  1. Administration of claims and payments

Incorrect claims can cost insurers, governments, and providers a lot of money, time, and effort. Machine learning can help to simplify claims and payment administration by, for instance, allowing for more accurate claims data and ensuring that claims are correct.

  1. Additional administrative procedures

Machine learning can be used in a wide range of administrative processes, such as claims processing, clinical documentation, revenue cycle management, and medical data management. It can even be used to create patient-facing tools like chatbots for telehealth, mental health and wellness support, and other general interactions that don’t require doctor input.

  1. Health policy and prediction

Machine learning holds enormous promise for predictive modeling and health policy. Population health machine learning models, for instance, can be used to predict which populations are at risk of certain accidents or conditions, as well as hospital readmissions. Similarly, using machine learning to identify trends in data on social determinants of health can inform policy. Governments and organizations could better target patients who are at a higher risk of developing preventable conditions such as heart disease and diabetes.

  1. Electronic health records

Machine learning can assist in making sense of the massive amounts of data now available via electronic health records (EHR). The majority of these are in the form of free-form text entries, also known as unstructured data. Machine learning has the potential to rapidly interpret this free-form data to glean valuable insights at scale for millions of patients, enabling better decision-making throughout the entire patient-care cycle.

  1. Diagnosis and treatment

Machine learning is being used more and more for diagnosis and treatment recommendations. Clinical decision support tools (CDS), in particular, can use machine learning to improve healthcare providers’ decision-making processes and provide the best possible care. CDS tools analyze massive amounts of data to make treatment recommendations. They can also alert providers to potential problems, allowing them to take preventative measures.

  1. Drug development

Machine learning is being used by researchers to create cohorts for costly clinical trials, paving the way for better studies and faster, more effective drug development. As a result, researchers can make data-driven decisions, identify key patterns and trends more easily, and achieve greater efficiency in their studies.

Machine learning and healthcare in the future

Machine learning is already beginning to realize its potential in healthcare, from improving drug research and development to improving patient care and administrative processes. Machine learning and other AI technologies are likely to see widespread adoption in the coming years. Instead of completely replacing clinicians, these technologies are more likely to supplement and enhance their roles. Long-term outcomes could include improved care quality and a more efficient and cost-effective healthcare system, both of which can only benefit patients, providers, insurers, regulators, and policymakers.

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