Since the release of OpenAI’s ChatGPT in 2022, the buzz around artificial intelligence has been impossible to ignore. From advertisements during the SuperBowl to webinars and working groups, the impact of artificial intelligence has been felt in almost every sector of our world.
But, what if we told you the most transformative shift is still on the horizon?
When ChatGPT first released it changed the way the world, including clinical research, worked. Now NVIDIA, one of the most premier companies leading the way in the development of AI, has stated that they expect the development of Agentic AI, a new type of artificial intelligence to “change the way we work in ways that parallel how different work became with the arrival of the internet.”
This means agentic AI may have a much bigger impact than even generative AI did years back.
So, if you’re curious about agentic AI, read on as we delve into its nature, differentiate it from generative AI, and reveal its transformative role in clinical research."
What exactly is Agentic AI? At its core, Agentic AI refers to an AI system that can act independently to perform tasks on behalf of a user or another system. Think of it as an AI with "agency.” This means it has the ability to analyze data, make decisions, and then act on those decisions.
As IBM puts it, Agentic AI relies on "a digital ecosystem of large language models (LLMs), machine learning (ML), and natural language processing (NLP) to perform autonomous tasks." Within this overarching concept, AI agents are the individual components or models specifically designed to handle particular tasks and processes. These agents have the power to analyze information and determine the next course of action based on their understanding.
It's important to temper expectations, though. While Agentic AI represents a remarkable advancement, it's still considered a “narrow intelligence” AI, also known as (ANI). This means it excels at specific tasks but doesn't possess the broad, human-like reasoning of artificial general intelligence (AGI), which remains a future aspiration.
As Jason Warrelmann, Vice President of Healthcare Strategy at UiPath, explains, "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. The computing required for that is still beyond us.” Thus, agentic AI is a very, very, powerful tool, but it cannot be thought of as a sentient mind.
Agentic AI vs. Generative AI: What is the difference? You're likely familiar with generative AI, which has already taken the world by storm over the last year due to its ability to create content.
Generative AI applications leverage data from LLMs to craft responses, with the quality often hinging on the specificity of the user's "prompt engineering." This means that it’s very reactive. You ask, and it generates. If you ask poorly, you may (or may not) receive a poor answer.
Of course, much like generative AI, agentic AI’s ability relies on the quality of the data it pulls from. However, Agentic AI is different from generative AI because it's proactive in the ways in which it can use that data. In other words, it builds upon the capabilities of Generative AI by adding a layer of sophisticated reasoning and autonomous action. Instead of just generating a response, an Agentic AI system has all of the following:
Autonomy : It can perform tasks all on its own, without needing constant human input.Reasoning : It can make smart, advanced decisions based on the information it processes.Flexible planning : If circumstances change, Agentic AI can adjust its plans on the fly to stay on track.Workflow optimization : It's excellent at efficiently handling complex, multi-step processes.Natural language understanding: You can give it complex instructions, and it will comprehend and follow them.System integration: It can seamlessly connect and work with various existing business systems.
The Everest Group shows the evolution of AI to agentic AI Last year, the Everest Group released a blog on agentic AI . In it, they showcase how the world moved from foundational intelligence models, to where we are today.
If you’re curious as to how we got here, take a look at the chart below from the Everest Group.
How agentic AI can help clinical trials These days, companies are starting to use Agentic AI to help streamline everything from patient recruitment to regulatory compliance. With its ability to act independently and adjust dynamically, Agentic AI isn't just supporting clinical trials – it's actively redefining them.
Here's how Agentic AI is reshaping key aspects of clinical trials:
Site and safety monitoring: Imagine an AI system that can predict potential risks and adverse events before they happen. Agentic AI does exactly that. Its abilities include generating real-time alerts and recommending immediate "next-best actions." This proactive approach significantly enhances patient safety and ensures timely interventions.Protocol setup: The often-cumbersome process of setting up trial protocols is being streamlined. Agentic AI can autonomously digitize, validate, and update protocols based on dynamic requirements, drastically reducing manual effort and ensuring adherence to evolving regulatory standards.Patient recruitment: Finding the right patients for a trial can be a major hurdle. Agentic AI-driven analytics help identify eligible participants with greater precision, forecast recruitment trends, and even automate personalized outreach and follow-ups. The result? Faster enrollment and improved patient engagement.Data management: Data is the lifeblood of clinical trials. Agentic AI continuously monitors, standardizes, and validates trial data in real-time across multiple sources. This ensures impeccable data integrity, minimizes errors, and accelerates critical decision-making throughout the entire trial lifecycle.Regulatory submission preparation: Preparing regulatory submission reports is a complex and time-consuming task. Agentic AI simplifies this by automating data collation and formatting. This not only enhances compliance but also significantly reduces administrative burden and speeds up crucial approval timelines.Predictive modeling: Leveraging vast amounts of historical data, Agentic AI can predict patient outcomes. This powerful capability helps researchers refine treatment protocols and optimize study designs in real-time, leading to a data-driven approach that boosts trial efficiency and improves success rates.Challenges that limit success with agentic AI Much like generative AI and other technologies, successful adoption of hinges on several critical factors:
High-quality and standardized data: Agentic AI is only as good as the data it's fed. Inconsistent data sources, like fragmented Electronic Health Records (EHRs) and lab reports, can lead to biases and inefficiencies. Standardization is paramount to ensure AI can process real-world evidence effectively and meet regulatory expectations.Robust information technology (IT) infrastructure: Processing the immense amounts of data generated in clinical trials requires a powerful IT backbone. Scalable cloud solutions are essential for real-time analytics, while secure storage is non-negotiable for safeguarding sensitive patient information. Without a well-built IT infrastructure, AI models will struggle with inefficiencies and cybersecurity risks.Seamless integration with clinical data systems: For Agentic AI to function effectively, it must connect smoothly with existing platforms like EHRs and Clinical Trial Management Systems (CTMS). Adopting modern data exchange frameworks is crucial to avoid workflow disruptions and ensure AI can interact seamlessly with trial data, leading to faster insights.Balancing automation with human oversight: While Agentic AI can optimize workflows and predict outcomes, human expertise remains indispensable. Complex decisions, ethical compliance, and error mitigation still require human judgment. A hybrid AI-human model is key to enhancing reliability, transparency, and trust in AI-driven clinical trials.Ensuring compliance with data and regulatory standards: The use of AI in clinical trials must strictly adhere to regulations like GDPR and HIPAA, which mandate rigorous data security and patient safety measures. With a growing emphasis on AI transparency and auditability, strong governance frameworks are needed to ensure AI decisions remain interpretable, compliant, and aligned with ethical standards.
Conclusion: AI’s big moment is here, now The advent of Agentic AI marks a pivotal moment in the evolution of artificial intelligence, particularly within the clinical research landscape. We've moved beyond AI that simply processes information to systems that can think, plan, and act autonomously, redefining efficiency and possibility. While the excitement surrounding Agentic AI is immense, its true potential will only be unlocked through a concerted effort to ensure high-quality data, robust infrastructure, seamless integration, and, crucially, a thoughtful balance of automation with indispensable human oversight and adherence to strict regulatory standards.
