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AI and Machine-Based Learning in Healthcare: Beyond the Buzzwords

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Part One: How We Got Here

This article, part one of two, seeks first to unpack the question: what is artificial intelligence (AI)? With a foundation established, we’ll look at the way it’s changing the healthcare industry in part two.

AI is not a new term; born in the 1950s, much of the early work in the field focused on strong (or general) AI – technology whose intellectual capability is functionally equal to a human’s. Unsurprisingly, progress was slow and attention quickly shifted to weak (or applied) AI – technology focused on addressing a narrowly defined task.

A means to the end of powering weak AI, machine learning rose to prominence in the 1980s to give “computers the ability to learn without being explicitly programmed” (Arthur Samuel, 1959). Machine learning is proving to be the most successful approach to powering modern AI and is where most of today’s advancements lie, even spawning related subfields such as deep learning and neural networks. Given that machine learning underpins modern AI, understanding it is critical to understanding why companies that harness it hope to make such an impact on the practice of medicine. Much of machine learning can be broken down into two disciplines: supervised learning and unsupervised learning. Unsupervised learning techniques are often used to organize and sanitize large datasets before using a supervised learning algorithm to discover new insights.

Read Part One

Part Two: Where We're Going

Having looked at the history of AI and machine learning, as well as some of the strategies employed by today’s data scientists, in the first part of this article, we’ll now explore how companies are using these techniques to transform the healthcare industry in part two. I recently spoke with three companies, each of whom are using AI and machine learning to power their products, push boundaries, and improve care for patients. While brought to life through unique experiences, each founder largely shared a common perspective on how the field has evolved, the challenges of applying it in healthcare, and what the future might hold.

Read Part Two

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