Agentic AI poised to transform clinical trials in pharma

Artificial intelligence (AI) is rapidly gaining traction in the pharmaceutical industry, with 70% of pharma executives surveyed by Define Ventures considering it an immediate priority. AI is already being used to speed drug development, assist with medical writing, and improve trial site and patient selection. Now, agentic AI—a more autonomous form of AI—is emerging as a game-changer for clinical research.

The Shift Toward Agentic AI

Traditional AI provides recommendations, but agentic AI can autonomously execute tasks like data analysis, enrollment monitoring, and compliance risk identification. According to IQVIA, these systems can take corrective actions without constant human intervention.

"An imperative of the use of agentic AI is to make [R&D] more efficient so that we can do more." — Andrew Mackinnon, Executive General Manager for Customer Value, Medable

Gartner predicts that by 2028, 15% of day-to-day work decisions will be made autonomously by AI.

Industry Applications and Risks

• Medable’s AI Agents: The company recently launched Agent Studio, the first agentic AI platform for clinical trials, along with CRA Agent, a monitoring assistant that automates data checks and site follow-ups.
• Risk Management: While AI excels at repetitive tasks, safeguards are needed to prevent errors or "hallucinations" (fabricating data). High-stakes decisions, like safety monitoring, still require human oversight.

The Future of AI in Pharma

• Slow Adoption in Patient-Facing Roles: Ethical concerns may delay AI in direct patient interactions.
• Faster Drug Approvals: AI could help clear the backlog in drug pipelines. The FDA approved only 50 novel drugs in 2024, but AI-driven efficiency could increase this number.

"That’s not enough. We need to be doing a much better job of putting drugs through that process," Mackinnon said. "Agentic AI can make it more efficient so that we can do more."

Key Takeaways

• Agentic AI automates tedious tasks, freeing humans for creative problem-solving.
• Implementation requires balance—AI should handle low-risk tasks while humans oversee critical decisions.
• Potential impact: Faster trials, more drug approvals, and reshaped roles for clinical staff.