Demystifying AI in Healthcare: The Top Seven Questions to Ask Your AI Vendor

There’s been a lot of buzz in healthcare about the potential of artificial intelligence (AI) to streamline administrative processes, while ensuring timely care delivery, resource allocation, and patient satisfaction. This, combined with market drivers, including the global tech surge, increased competition, and the popularity of generative AI, have created a perfect storm of interest in AI technology, along with a myriad of companies offering AI solutions to fill this demand. However, despite this excitement, not all AI solutions are created equal.

For executives in an industry as unique as healthcare, where errors can have dire consequences, the stakes are especially high. Skepticism centers around the role and potential risks of AI. It is critical for both providers and payers to have a firm understanding of the various types of AI technology available to them, so they can best assess potential AI solutions for their various care determination and delivery workflows.

This blog breaks down the fundamentals of AI – offering an overview of the most common technologies available in the market and a list of questions to ask vendors when evaluating AI solutions.

AI 101: Breaking Down the Different Types of AI

AI technologies can be categorized into three groups: analytical AI, reactive AI, and generative AI. Solutions leveraging analytical AI analyze and interpret complex data sets, uncover insights, and make data-driven predictions or decisions. Reactive AI technologies operate based on predefined rules and are designed to perform specific tasks without the ability to learn or adapt.

Generative AI technologies generate new content including audio/video images, and text by drawing from learned patterns in existing data using Large Language Models (LLMs). LLMs use complex statistical methodologies to process natural language inputs and attempt to predict the next best word to respond to a prompt based on the data it’s been trained on. It then predicts the next word, and so on, until its answer or response is complete. While the ability of LLMs to understand language and generate answers relevant to the conversation’s context is remarkable, there are many limitations, including a propensity for “hallucinations,” which can result in factually incorrect responses. Also, LLMs are limited to the information provided to them when they are trained. Since these systems make determinations based on training data that may reflect human biases, generative AI has the potential to perpetuate societal biases as well. Furthermore, the “black box” nature of some generative AI systems has created growing concerns due to the inability to see how those AI systems make their decisions.

Questions to Ask Your AI Vendor

Given the inherent biases of AI and risk of inaccuracies, when evaluating AI solutions to meet organizational needs, healthcare organizations should be cautious to ensure responsible and ethical use of AI.

Below are the top questions to ask your AI vendor during the exploration process.

For a full breakdown of the information detailed in this blog, download our Questions to Ask Your AI Vendor Cheat Sheet . Also visit Availity.com/AuthAI to learn how Availity is leveraging analytical AI to streamline prior authorization reviews.