6 Things to Know About Responsible Generative AI In Healthcare Marketing

It’s no exaggeration to say 2023 has been the Year of AI — and the use of AI technologies in healthcare marketing is no exception.

// By Elaine Christie //

You can probably rattle off a host of marketing benefits. There are seemingly unlimited new AI tools every day to accelerate strategic priorities and get more work done in less time (boom!). Such tools not only enhance the impact of campaigns, but they can also contribute to a more patient-centric approach by tailoring communication and services.

So, what’s not to love about using AI in healthcare marketing? While AI presents numerous opportunities, some of the big concerns include privacy and data security, ethical considerations, and algorithm biases (which we’ll look at in greater detail below).

Predictive vs. Generative AI

First, let’s define the core differences between predictive and generative AI. Predictive artificial intelligence is used for forecasting or making predictions. This approach has been used for many years in healthcare. For example, exposing an algorithm to previous data to identify trends. Think of how the health insurance industry uses statistics, analytics, and machine learning to analyze data and predict the likelihood of a future event.

It can help to think of predictive AI as the tried-and-true approach, whereas generative AI is more akin to the Wild West.

Matt Cohen, Director of AI at Loyal

During a recent eHealthcare Strategy & Trends webinar, healthcare industry veteran Rachelle Montano, Loyal’s vice president of clinical strategy, and Matt Cohen, director of AI at Loyal, looked at the pros and cons of artificial intelligence in healthcare marketing.

“Generative AI is kind of the newest buzz concept,” says Cohen. “It’s not anti-generative or pro-generative, but rather, it’s understanding the nuances.”

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