The Potential for Artificial Intelligence and Machine Learning in Developmental Healthcare

Artificial intelligence is the ability of a computer program to learn and make decisions for itself. That might sound like science fiction, but it's already being applied in a variety of industries and is contributing to life-changing innovations. In recent years, there have been rapid increases in the uses of artificial intelligence in healthcare, but it is still underutilized in the developmental healthcare sphere.

In this article, we provide an overview of artificial intelligence and its subset, machine learning; describe their uses and potential uses in healthcare; and predict how they could be used to fix some of the problems that contribute to a critical–and potentially detrimental–care gap in developmental healthcare.

In this article:

What is artificial intelligence and how does it work?

How are artificial intelligence and machine learning used in healthcare?

The benefits of AI technologies in healthcare generally

Current applications of machine learning in healthcare

A word of caution

Artificial intelligence and machine learning in pediatrics

Potential uses of artificial intelligence and machine learning in developmental healthcare

The takeaway

What is artificial intelligence and how does it work?

At its most basic level, an artificial intelligence (AI) system works by taking in huge amounts of data and using that data to improve its performance at a specific task.

For example, imagine that you're training an AI system to identify dogs in pictures. You would start by feeding it lots of pictures of dogs, along with pictures of other things (such as cats, houses, trees). The AI system would then use what it knows about the world (dogs have four legs, tails, etc.) to try to identify which pictures contain dogs.

Over time, as the AI system is exposed to more and more data, it will get better and better at identifying dogs, to the point where it can easily distinguish a dog from other objects.

By feeding an AI system enough data, we can teach it to do all sorts of things, from recognizing faces to driving cars.

What is machine learning?

There are two main types of AI systems:

• rule-based systems

• machine learning (ML) systems.

Rule-based systems are exactly what they sound like–they follow a set of rules that have been programmed into them by humans.

ML systems, on the other hand, don't rely on human-created rules, but teach themselves from examples. Simply put, ML is a method of teaching computers to learn from data, identify patterns, and make predictions. This is done through algorithms, which are a set of rules that can be followed to solve a problem.

So, going back to our dog example, an ML system would learn to identify dogs by looking at lots of pictures of dogs (and non-dogs), rather than being given a set of rules about what makes something a dog.

Importantly, ML algorithms generally improve as they’re given more data. This means that the more pictures of both dogs and non-dogs the algorithm is given to work with, the better it will become at identifying dogs.

ML is not a new field–it’s been around for decades. However, it has become more prominent in recent years due to the increasing amount of data that is available and the advances in computing power and storage.

Because it can handle–and even requires—such vast amounts of data, ML is often used for tasks that are too difficult or time-consuming for humans to do.

What is deep learning?

Deep learning is a subset of ML that enables computers to learn from data that is unstructured or unlabeled, also known as unsupervised machine learning.

This might sound confusing, so let's break it down further.

As we discussed above, ML is a method of teaching computers to recognize patterns. Deep learning takes this one step further by teaching computers to not only recognize patterns, but also to make predictions based on those patterns.