27 Apr Top 4 Applications of AI and Machine Learning in Healthcare
When it comes to innovations in the healthcare industry, it is evident that technologies such as artificial intelligence (AI) and related fields like machine learning (ML) have provided largely significant contributions to its development. In fact, the global healthcare AI market is expected to reach $51.3 billion by 2027. This growth is largely attributed to three things AI and ML have introduced to the industry: lower healthcare costs, personalized care, and an easier way to draw insights from patient datasets.
Are you interested to see how both technologies have done this? Here are some of AI and ML’s top applications in healthcare:
Personalized medical insights
Every patient is built differently, whether this is because of their genetic background, medical history, or their lifestyle. As such, no single treatment will fit every person. Fortunately, one of the biggest applications of AI and ML is personalized treatments. The solutions offered by IBM Watson Oncology are a good example of this. They have ML algorithms that can sift through the patient’s medical history to advise professionals on the best treatment options.
In fact, have you heard about remote patient monitoring (RPM) devices? These are technologies that are capable of collecting health data and feeding them into AI software, producing personalized medical insights. The smartwatches by Somatix fall under this category. By sensing the way users move their hand, its AI is able to determine if they’re smoking, drinking enough liquid, walking enough miles, and other things.
Medical imaging has been a huge help in human diagnostics since it was invented in the late 18th century. Imaging devices like x-rays, PET scanners, and CT scanners are manufactured with powerful PCB designs that are wired with copper paths, capacitors, and other components of a tiny but high-density board layout. This allows manufacturers to fit a number of manufacturing-specific features into them, such as cameras that can see through skin and high-frequency sound waves.
And while the human eye is capable of spotting the problems in the images taken, AI can be built to recognize common discrepancies, making patient assessments much faster. For example, Akvelon built an AI model that can pinpoint distal radius bone fractures in medical images.
Healthcare facilities hold a lot of sensitive patient data, so it’s important to have the right cybersecurity protocols in place. And since most healthcare data is now stored in the cloud for easy access, providers need a tighter cloud security framework, which can luckily be done with AI and ML algorithms. For instance, NVIDIA recently unveiled “Morpheus” — an AI-driven cloud-native app framework that detects potential data leaks and other cybersecurity threats.
There’s also a huge movement to incorporate ML-based learning techniques, such as Support Vector Machines and Fuzzy C-means Clustering, into healthcare cloud systems for more secure data processing.
The first (and arguably the most time-consuming) step in drug discovery is determining the right molecules that can bind themselves to a target protein. Together, they should form the “cure” to whatever malign disease the research team is designing the drug for. As long as the system has a working database, ML algorithms can accelerate the discovery process by predicting how molecules bind. Google’s AI subsidiary, DeepMind, very recently launched “AlphaFold” — an ML system that does exactly this.
AI and ML have a lot to contribute to healthcare, from patient outcomes to drug discovery. And with continuously evolving algorithms, their provided solutions can be more refined, leading to further advances in healthcare and other related fields.
Article exclusively submitted to akvelon.com
Written by Rita Jordan
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