What Is Agentic AI and How Can It Be Used in Healthcare?

What Is Agentic AI?

Like other forms of artificial intelligence, agentic AI is only as accurate as the data that fuels it. It relies on “a digital ecosystem of large language models (LLMs), machine learning (ML), and natural language processing (NLP) to perform autonomous tasks on behalf of the user or another system,” according to IBM. 

Agentic AI is the term used to describe the overall concept. AI agents are the individual components within the model that are created to handle specific tasks and processes. Agents within an agentic AI system have the “agency” to analyze data and then make decisions about what to do with the results.

While a significant step forward, both Saunders and Jason Warrelmann, vice president of healthcare strategy at UiPath, caution that agentic AI is still considered artificial narrow intelligence. Artificial general intelligence, which would allow machines to think like humans, does not yet exist.

“Right now, the best we can do is provide context so that the agent understands how to answer. There’s still a large language model behind it, so the agentic AI isn’t acting completely on its own,” Warrelmann says. “The computing required for that is still beyond us.”

“While agents and reasoning are powerful capabilities, they’re still no match for the incredible complexity of human intelligence,” Saunders agrees.

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How Does Agentic AI Differ from Generative AI?

Generative AI applications use data from large language models to craft responses. The quality of the output relies largely on the specificity and guidance provided by the user, a process known as prompt engineering.

Agentic AI is more proactive. It can pull information from multiple sources, use sophisticated reasoning and then automatically complete the next task. 

“Agentic AI builds on generative AI, taking simple responses further with the ability to consider options, go back and redo steps,” says Saunders. “It works much more like we do when we solve problems and work out how to consider new information.” 

In healthcare, agentic and generative AI can work together to increase efficiencies and boost productivity. For example, after a surgery, generative AI can use the patient’s record and the surgeon’s notes to write post-op instructions for medication use, activity limitations and follow-up care.

Agentic AI can then share the generated instructions, monitor if the patient has accessed the document within the patient portal and send reminders about future appointments. If the patient reports a serious symptom, the healthcare AI agent could automatically alert a nurse or schedule a virtual consult with the provider.

How Does Agentic AI Work in Healthcare?

Healthcare AI agents have many applications across the medical industry, including:

• Allowing scientists to develop new therapeutics faster by screening billions of compounds and testing which combinations would be most effective
• Helping researchers conduct clinical trials by identifying qualifying patients and monitoring participants for positive or adverse effects
• Analyzing insurance denials, providing examples of similar situations and producing appeals that can be sent to payers automatically
• Helping with clinical referrals and diagnoses by reviewing patient charts and recommending treatments
• Acting as a virtual health assistant with real-time monitoring and medication and appointment reminders 

According to a report released by the American Hospital Association, more than 40% of total hospital expenses are administrative costs. Warrelmann says he expects that in the near future, healthcare organizations will adopt AI agents on a wider scale to enhance their back-office operations. 

“Hospitals have to figure out staffing, salaries, bed utilization rates, inventory management and quality protocols,” he says, explaining that AI agents can quickly analyze all of that data and provide recommendations for how the hospital could be more efficient.

DISCOVER: Here are 13 ways AI enhances healthcare operations, patient care and treatments.

What Should Healthcare IT Leaders Know About Agentic AI?

Because AI agents can operate autonomously, data quality and oversight are even more critical. Warrelmann stresses that healthcare and IT leaders who introduce agentic AI into their operations need to keep a close eye on where the AI is pulling information. 

For example, while healthcare AI agents may be allowed to ingest data from an electronic medical record, they should be blocked from accessing private email exchanges. “You start to get into segmentation of data and making sure confidential information doesn’t end up in the wrong place,” says Warrelmann.

Saunders adds that heath systems may be able to rely on their existing partnerships with third-party vendors to implement agentic AI. “Many technology providers are now building agents into their own platforms, which provides a simple way for healthcare IT leaders to use agents in the systems they’re already working with to run their operations.” 

These technology platforms have integrated agentic AI into their systems:

• NVIDIA NeMo
• AutoGen by Microsoft
• IBM watsonx Orchestrate
• Gemini 2.0 by Google
• UiPath Agent Builder

“Agentic AI is already transforming enterprises and is likely to be a multitrillion-dollar opportunity,” says Saunders. “This means that healthcare IT leaders should lean in to learn how AI agents can help transform work across drug discovery, patient care, operations and so much more.”