Large Language Models (LLMs), such as ChatGPT, Anthropic’s Claude, Meta’s Llama, and many others, are designed to develop human-like intelligence by utilizing contextually relevant text and training itself from the massive amounts of content made available to the tool. By leveraging LLMs, healthcare providers can efficiently extract relevant information, identify patterns, and gain insights in a matter of seconds, which gives the providers more time to focus on patient outcomes.

Over the past couple of years, healthcare organizations have been making significant investments in the world of artificial intelligence (AI). We have seen a number of avenues different organizations are taking with their new AI tool: from interpreting medical images to integrating LLMs into their electronic medical records (EMRs).

And it’s not just the large healthcare organizations that are looking to invest in AI as a future value driver, so are healthcare startups. In fact, a recent report by Silicon Valley Bank (SVB), a division of First Citizens Bank, shows that $2.8 billion has already been invested in AI healthcare companies as of June 2024, with SVP projecting that figure to reach over $11B in venture capital investments by year-end, the highest it has been since 2021.

So, the race to utilize AI is upon us, with LLMs being the chosen driver for early adoption. But before healthcare organizations can truly implement this new tool, specific security criteria need to be established within their organization. The Open Worldwide Application Security Project, or OWASP, is a nonprofit foundation that provides an annual standard awareness document for developers and web application security.

This important document from OWASP identifies 10 of the areas of concern for those who are deploying and managing LLMs. Our experts here at Optimum Healthcare IT reviewed OWASP’s list and developed responses to help guide your team’s journey into LLMs.