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The Difference Between AI and Machine Learning in the Healthcare Space

In living memory, healthcare has transformed almost beyond recognition thanks to the introduction of technology. Imaging scanners, electronic health records, remote monitoring tools, genome sequencing, and other technologies have improved the efficiency of healthcare providers and delivered invaluable insight into disease and health. We are on the verge of another outstanding leap forward in healthcare technology thanks to the introduction of artificial intelligence (AI) and machine learning (ML). Currently, many healthcare professionals and administrators seem to use these terms interchangeably, but there are subtle differences to be aware of before implementing AI and ML tech.

ML in Healthcare

Too often, when people are discussing artificial intelligence programs in use today, they are in fact talking about machine learning. As the name suggests, machine learning concerns building machines capable of extracting knowledge from data. ML programs rely on algorithms that allow for predictions based on datasets. The more data an ML program has available for analysis, the better its predictions are likely to be.

A computer’s ability to learn isn’t particularly new; the first algorithm capable of learning was developed in the 1950s, a program designed to play checkers. In the 1980s and 1990s, ML flourished with the popularity of neural networks. And with improvements to ML algorithms through the 2000s, ML tools have spread into almost every industry, including healthcare.

There are dozens, perhaps hundreds of examples of ML tools used in healthcare. Some of the most common include tools used to aid providers in diagnosis: Algorithms can assist doctors in reviewing various tests, especially imaging, to identify disease, and using predictive analytics, machine learning tools can anticipate which patients are more likely to develop certain diseases. ML can also be invaluable in assisting with healthcare administration, improving efficiency with patient care, developing new pharmaceuticals and treatment protocols, and more.

Various tech operations are involved in creating new ML tools and improving those tools already in use. Machine learning powerhouses like Google, Microsoft, and IBM, as well as tech startups and bioinformatics companies, are all searching for the next great application of ML. In fact, it is almost entirely ML tools that are currently driving the immense growth of the healthcare artificial intelligence sector. One report suggests that the healthcare AI market could be worth roughly $8 billion in 2022 — which is astonishing considering that true AI is as yet utterly absent in the healthcare industry.

AI in Healthcare

Artificial intelligence is just that: a computer system that mimics human intelligence. Machine learning algorithms can look at data and extract information that can impact its recommendations for human action, but AI tools are intended to perform uniquely human tasks, like thinking and reasoning, and most importantly, AI can make its own decisions and alter its behavior.

In healthcare, AI could be invaluable for any number of tasks, like performing delicate, complex surgeries, prescribing routine drug refills, or developing personalized treatment plans.

However, when one learns about the state of AI in healthcare, it is clear that the industry has yet to receive sophisticated intelligent machines capable of such tasks. Though tech companies are racing to develop AI tools for healthcare, there aren’t any examples of healthcare AI systems that are robust enough for use. When AI becomes advanced enough to make critical healthcare decisions, they are more likely to work alongside healthcare providers than replace them. Patients demand human interaction in their healthcare experiences, and AI will never be able to replicate the human touch to the degree that healthcare requires.

The distinction between AI and ML might seem like one based on semantics, but to data scientists and healthcare practitioners, it is an important distinction to make. ML tools are already available to many within the healthcare field, and they certainly benefit providers and administrators. Meanwhile, the promised value of AI is intriguing, it has yet to manifest. Undoubtedly, the coming years will bring about significant technological advancement, and AI will truly become available throughout healthcare — but until then, everyone should take care to remember the differences between machine learning and artificial intelligence.

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