Updated: Jul 21
Artificial intelligence and machine learning have gained serious traction in the healthcare world of late, but there still seems to be a lot of mystery around what those two terms actually mean. Both artificial intelligence and machine learning are pillars of digital transformation that are changing healthcare and other industries. Without a full understanding of the two terms, the differences in algorithms, and how they are applied, it’s hard to appreciate just how much the technologies are capable of.
Banjo Health is dedicated to transforming healthcare through innovative technology. Part of doing so is demystifying machine learning and artificial intelligence and exploring the differences to understand their application in a healthcare setting better.
We’ll start with some basic definitions before delving into some of the key differences between algorithms, such as traditional decision versus true artificial intelligence.
Defining artificial intelligence and machine learning
Before we go into the nuts and bolts of artificial intelligence and its subsets, we’ll start with a few definitions.
Artificial intelligence is the “ability of a computer or computer-controlled robot to perform tasks commonly associated with intelligent beings.” It is essentially when a computer is trained to perform tasks that humans would typically complete.
Machine learning is a subset of artificial intelligence, where machines learn independently without needing to be programmed. It is an AI application used so that systems can automatically and continually learn from experience during use.
What's the difference between using a traditional decision tree algorithm versus true AI?
Now that you have an idea of the terms, it’s time to look at more specifics. True artificial intelligence differs from traditional decision tree algorithms, but it might not always be clear why that’s the case.
True artificial intelligence comes in two forms. Systems or devices that can handle any task (i.e., generalized AI) and systems used in software for a specific purpose (i.e., applied AI).
Applied AI is what you’ll commonly find, and it can be used for intelligent automation, reduce errors and predict outcomes.
Traditional decision tree algorithms, however, are a machine learning program used for powerful analytical modeling. Traditional decision tree algorithms are not used automation and error detection but are instead used for classification and regression.
While both true artificial intelligence and decision tree algorithms can comprehend large datasets without an issue, they differ in how the data is used. True artificial intelligence will be looking at ways to reduce errors, automate tasks, and predict general outcomes based on the data set.
On the other hand, traditional decision tree algorithms use a flowchart-like structure to understand possible effects, decision costs, and visualizing a decision based on multiple factors.
Using both true artificial intelligence and traditional decision tree algorithms can help bring complex data to life and provide a holistic view into each aspect of the data where improvements can be made.
While true artificial intelligence will help refine and improve operations, decision tree algorithms can use your data to aid with multi-factor decision-making using if/then rules.
Having both true artificial intelligence and traditional decision-tree modeling can be incredibly helpful for industries such as healthcare, where there is an immense amount of data to process and complicated decisions to make in areas such as prior authorization.
However, by using this kind of technology, the administrative burden can be significantly reduced while creating a better experience for patients, all in one fell swoop.
At Banjo Health, we’re using both true artificial intelligence and decision tree algorithms to change prior authorization for the better. To learn more about our innovative technology and what our software is powered by, please get in touch!