Machine Learning in Healthcare: 10 Future Trends To Watch Out

May 20, 2022
4 min read

A dynamically developing technology with significant implications, machine learning, is already helping the industry deal with some of its most challenging problems, such as sorting through vast amounts of patient data and boosting the quality and customization of treatments and care. So what exactly is machine learning, and what are some of the most important machine learning trends in healthcare? We look at how it's already improving healthcare and what the future holds for it.

What is Machine Learning?

Machine learning is a sort of technology that belongs to the artificial intelligence technology cluster. According to one definition, machine learning is a statistical method for applying models to data, and then computer learning is accomplished by training models on this data. In addition, machine learning also refers to systems, apps, or algorithms that can recognize patterns in large amounts of data and generate predictions. Another approach to thinking about machine learning is to think of it as creating algorithms and programs based on historical and real-time data.

Technology ultimately benefits more than just the healthcare industry. For example, the agriculture, manufacturing, hospitality, retail, and finance sectors use data science and machine learning tools. Moreover, machine learning can also be used in non-profit endeavours like humanitarian help.

Here are some of the important ML trends in the healthcare industry:

1: Healthcare Personalization and Precision Medicine:

Precision medicine is already widely used in machine learning. It uses patient data and the treatment context to predict successful treatment protocols. Precision medicine allows for very specific, individualized treatment strategies, which can improve clinical outcomes.

2: Bioprinting and Organ Care Technology:

The global transplantation industry is estimated to reach $26.5 billion by 2028, making organ transplants a critical component of healthcare. Matthew J Everly reports that approximately 2,000 heart transplants occur in the United States each year. However, over 50,000 people are predicted to require a heart transplant. What can be done to assist all of these heart disease patients?

Improving organ care technologies is one way to solve this issue. This involves looking after the organ when it is not in the body. Using a Transmedics Organ Care System, the Wexner Medical Center at Ohio State University can keep a heart, lung, or liver external for several hours by providing adequate care, heat, and nutrition.

There are other options besides keeping organs alive outside the body. The fact that 3D-printed organs are a genuine technology, albeit still in development, has been applied in clinical settings. Organs such as ears, corneas, bones, and skin are in the process of being 3D bio-printed.

The process is quite similar to ordinary 3D printing. Make a computer model of the tissue start. Because the bio-ink used in the printing process is made up of physically living cells, special attention to resolution and matrix structure is essential. They must then use stimulation to test the organ's functionality.

3: Applications of Categorization:

Applications for categorization include determining whether or not a patient will develop a specific ailment. This information can be used to help providers plan for capacity and improve policy and effective prevention initiatives.

4: Image Evaluation:

Machine learning in healthcare is already being used to evaluate radiology and pathology images. It's also used to classify large numbers of photos swiftly. Machine learning for these procedures could become much more complex and accurate in the future.

5: Claims and Payment Management:

Incorrect claims can waste a lot of time, money, and effort for insurers, governments, and providers. Machine learning can streamline claims and payment administration by facilitating more accurate claims data and ensuring that claims are correct.

6: Other Administrative Procedures:

Revenue cycle management, clinical documentation, and medical data management are just a few of the administrative operations that can benefit from machine learning. It can even be used to create patient-facing solutions like telehealth chatbots, wellness support, mental health and other additional exchanges that do not necessarily require the participation of doctors

7: Health Policy and Prediction:

Machine learning has enormous potential for health policy and predictive modelling. Machine learning models for population health, for example, can be used to forecast which populations are at risk of specific accidents or illnesses, as well as hospital readmissions. In the same way, combining data on social determinants of health with machine learning to spot trends might help policymakers. Patients at an increased risk of avoidable illnesses like heart disease and diabetes should be better targeted by governments and organizations.

8: Electronic Health Records (EHRs):

Machine learning has the potential to make sense of the massive amounts of data made available by electronic health records (EHR). The vast majority of these are free-form text submissions, often known as unstructured data. Machine learning has the ability to analyze this free-form data quickly and derives significant insights for millions of patients, allowing for enhanced decision-making across the patient-care cycle.

9: Treatment and Diagnosis:

Machine learning is continuously being developed to make therapy suggestions and diagnoses. Clinical decision support systems (CDS), in particular, can use machine learning to improve the decision-making processes of healthcare providers for the best possible care. To produce effective treatments, CDS technologies examine vast amounts of data. They can also notify providers of possible problems so that they can take preventative measures.

10: Drug Research and Development:

Researchers use machine learning to create cohorts for costly clinical trials, allowing for better studies and faster, more effective drug development. As a result, researchers may make data-driven decisions and more readily detect critical patterns and trends, resulting in increased study efficiency.
The Future of Healthcare and Machine Learning
From more effective medication research and development to patient care and administrative processes, machine learning is already getting started to realize its potential and importance in healthcare. Machine learning and other AI technologies are projected to become more widely adopted in the future years. These technologies are more likely to complement and enhance clinicians' jobs than fully replace them. Long-term benefits to patients, providers, regulators, insurers and legislators might include enhanced treatment quality and a more efficient and cost-effective healthcare system.