1. Identifying Diseases and Diagnosis
One of the chief ML applications in healthcare is the identification and diagnosis of diseases and ailments which are otherwise considered hard-to-diagnose. This can include anything from cancers which are tough to catch during the initial stages, to other genetic diseases. IBM Watson Genomics is a prime example of how integrating cognitive computing with genome-based tumor sequencing can help in making a fast diagnosis. Berg, the biopharma giant is leveraging AI to develop therapeutic treatments in areas such as oncology. P1vital's PReDicT (Predicting Response to Depression Treatment) aims to develop a commercially feasible way to diagnose and provide treatment in routine clinical conditions.
Healthcare is an important industry which offers value-based care to millions of people, while at the same time becoming top revenue earners for many countries. Today, the Healthcare industry in the US alone earns a revenue of $1.668 trillion. The US also spends more on healthcare per capita as compared to most other developed or developing nations. Quality, Value, and Outcome are three buzzwords that always accompany healthcare and promise a lot, and today, healthcare specialists and stakeholders around the globe are looking for innovative ways to deliver on this promise. Technology-enabled smart healthcare is no longer a flight of fancy, as Internet-connected medical devices are holding the health system as we know it together from falling apart under the population burden.
From playing a critical role in patient care, billing, and medical records, today technology is allowing healthcare specialists develop alternate staffing models, IP capitalization, provide smart healthcare, and reducing administrative and supply costs. Machine learning in healthcare is one such area which is seeing gradual acceptance in the healthcare industry. Google recently developed a machine-learning algorithm to identify cancerous tumors in mammograms, and researchers in Stanford University are using deep learning to identify skin cancer. Machine Learning (ML) is already lending a hand in diverse situations in healthcare. ML in healthcare helps to analyze thousands of different data points and suggest outcomes, provide timely risk scores, precise resource allocation, and has many other applications. In this article we will discuss some of the top applications of machine learning in healthcare, and how they stand to change the way we visualize the healthcare industry in 2018 and beyond.
Top 10 Applications of Machine Learning in Pharma and Medicine
The increasingly growing number of applications of machine learning in healthcare allows us to glimpse at a future where data, analysis, and innovation work hand-in-hand to help countless patients without them ever realizing it. Soon, it will be quite common to find ML-based applications embedded with real-time patient data available from different healthcare systems in multiple countries, thereby increasing the efficacy of new treatment options which were unavailable before.
Here are the top 10 applications of machine learning in healthcare -
Reap the Benefits of Machine Learning in Healthcare by Partnering With FWS
At Flatworld Solutions, we believe that healthcare providers need to stop considering machine learning as a concept from the future and instead embrace the real-world tools it is making available to us today! Over the years, we have helped global healthcare clients leverage the latest in technology to help patients and stakeholders alike. When it comes to machine learning, we find specific use cases in which ML-based applications can provide something of tangible value to your healthcare initiatives, and then help develop a step-by-step process to incorporate the same within your processes.
Our healthcare services have helped numerous Fortune 500 companies save time and costs while revitalizing their offerings for the modern world. To learn more about our services and how we can help customize solutions based on your requirements, contact us now!
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