Top 5 Applications of Machine Learning in Healthcare
Machine learning (ML) is a branch of artificial intelligence (AI), where computer systems independently find solutions to complex problems using recurring patterns in databases. Put differently, machine learning helps IT systems to recognize patterns from existing algorithms and datasets, then go ahead and develop appropriate solutions. Today, the healthcare sector is the leading industry in the adoption of machine learning technology in areas such as the treatment of chronic diseases, the development of advanced medical procedures, and the handling of patient data and records. Healthcare specialists in this space are continuously looking for more innovative ways to deliver value-based care to millions of people around the globe.
In this article, we are going to look at the top five applications of machine learning in healthcare and its potential in revolutionizing the industry. Some of the key areas this blog is addressing include the following:
- An overview of machine learning
- The healthcare’s potential with the adoption of machine learning
- 5 real-world applications of machine learning in healthcare, benefits, and use cases
- Trends and predictions on the future for healthcare with machine learning
What is Machine Learning?
Machine learning is a type of AI, a powerful technology that enables machines to process and finds patterns in large datasets for decision-making with little-to-no human intervention. Machine learning has evolved over time, starting from pattern recognition and learning without programming up until the current AI-enabled learning. IT systems are adapting independently with the exposure to new data. Their continuous learning from previous computations helps them produce accurate, reliable decisions and results. It is safe to say that this technology is not new because machine learning algorithms have been around for a while, but it is one that has developed tremendously with time. For instance, the ability of machines to automatically solve complex problems through the application of mathematical calculations to big data, repeatedly and quickly, is a new development. Moreover, these algorithms have improved their prediction accuracy significantly over the years without programming intervention.
So what makes machine learning important in today’s real-world settings? The undeniable difference this technology has brought to enterprises is its strong support for the development of new products as well as a view of trends in business operations and customer behavior. Major tech companies such as Google and Facebook are using machine learning to find an edge over the competition and have made this technology a key differentiator in their business models.
The Prospect of Machine Learning in Healthcare
Machine learning is aimed at training models to begin recognizing patterns using training data. A 2018 survey by Deloitte revealed 1,100 US companies were using AI, with 63% of them focusing on machine learning. The survey also indicated the wider applicability of this technology across a variety of industries and use cases. In healthcare, machine learning can take over routine tasks such as management of patient records, claims processing, and other clinical documentation. The key objective is to reduce human labor in administrative and regulatory duties that machines can do more efficiently.
Machine learning offers an opportunity for the healthcare industry to improve its overall health outcomes in hospitals, resulting in higher patient satisfaction. The use of AI-powered chatbots, for example, can provide a more personalized and convenient healthcare experience. In another 2019 survey by Pegasystems on 2,000 healthcare consumers, 42% of the patients said they are comfortable with their healthcare specialists using AI to make healthcare decisions. The bottom line is that patients today want a quick and personalized treatment process, and machine learning can address those desires.
Machine Learning Applications in Healthcare
As patients demand more personalized and convenient healthcare, machine learning is leading the way, with a rising number of ML-powered applications finding their way into healthcare systems around the world. Technology-enabled healthcare is becoming a reality and a necessity to meet the market demands, and the introduction of smart devices is becoming mainstream. Innovation in healthcare, undoubtedly, is shaping the future in treatment processes and medical research.
Here are the leading 5 applications of machine learning in healthcare:
Disease and Diagnosis Identification
Machine learning is highly efficient in identifying and diagnosing diseases, a process regarded as hard to do through conventional means. Diagnosing cancer at the initial stages, for instance, is not an easy process, and the same applies to other genetic ailments. IBM Watson Genomics best illustrates how cognitive computing is combined with genome-based tumor sequencing to give a quick diagnosis. Berg is also another biopharma company using AI to introduce therapeutic treatments in oncology, among other areas.
Medical Imaging Diagnosis
The InnerEye project by Microsoft is an example of how machine learning combined with deep learning birthed computer vision, a technology being used by the company to process medical images to make a diagnosis. Other use cases of image annotation include teeth segmentation and quantitative cancer cell detection. Soon, more AI-driven diagnostic processes will emerge as machine learning adoption continues to grow and becomes more accessible.
Personalized Treatment
Personalized treatment means pairing a patient’s health with predictive analytics. IBM Watson Oncology is a leader in this area by using patient medical history to provide multiple treatment plans. Personalized treatment will become even better as more advanced biosensors enter the market, availing more data for ML algorithms.
Disease Prediction
Diabetes is a common yet dangerous disease. It is known to cause other serious health conditions such as vision loss, heart and kidney diseases. An early diagnosis of this disease can potentially save lives. Naive Bayes is a classification algorithm in machine learning that can be used as a basis to develop a system that can predict diabetes. Naive Bayes is known to be more efficient than other algorithms such as KNN and Decision Tree when it comes to performance and computational time.
Clinical Research and Trial
Clinical trials take the longest time to complete, with massive resources invested in the process. With machine learning, predictive analytics can be used to identify the best clinical trial candidates using various data points such as social media activity and previous doctor visits. Moreover, machine learning will also minimize data-based errors significantly and ensure real-time monitoring and data access of the clinical trial participants.
The Future of Healthcare with Machine Learning
A recent study from Accenture estimates $150 billion annual savings in the US from AI applications in healthcare by 2026. AI and ML are expected to automate the majority of routine tasks, consequently giving an opportunity for human professionals to take on more complex tasks. Today, major tech companies have launched massive AI and ML-powered projects into the healthcare system, such as Microsoft’s InnerEye, IBM’s Watson Genomics, and Watson Oncology, and more collaborations between tech giants are noticeable. This said machine learning is the future in healthcare as scientists and researchers develop advanced algorithms to provide AI-driven solutions to daily tasks.
Conclusion
Machine learning is a vital technology for creating a sustainable competitive advantage across industries, not just in the healthcare industry. From automating routine tasks in administration to diagnosing genetic diseases, the healthcare industry is shifting completely to a new realm that will improve the overall healthcare operations. In that sense, machine learning does not cease to transform the future reality providing an array of opportunities for further advancement in the field.
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