Agentic AI made for clinical trials
Unlock a better way to run trials. Agents help with the manual work, coordination, and day-to-day friction so teams can focus on moving studies forward and making faster decisions.
Built specifically for clinical development with deep GxP, regulatory, and operational expertise, Agent Platform helps run better trials so you can find cures faster.
Launch quickly. Operate intelligently.
1: Connect
Connect your clinical systems to Agent Platform to make faster sense of your data, eliminate manual stitching, and enable real-time data flow for faster decisions.
2: Assist
Use our pre-configured agents to automate filing, proactively identify site risks, generate pre-visit summaries, or build your own agent exactly to your trial’s protocols and needs.
3: Verify
Check your work and get going. Whether you want your agentic teammates to act autonomously or operate with a human in the loop, they move work forward in real time.
4: Evolve
Go beyond static systems with a platform that evolves continuously, with built-in verification and quality assurance designed for clinical development.
Connect your clinical and enterprise systems
Medable connects with the clinical and enterprise tools your organization already uses, right out of the box. Need more? Add the connectors your workflows demand.
- Prebuilt integrations for common clinical and enterprise systems
- Fast onboarding with minimal IT lift
- Flexible connectors that adapt to your workflows
- Secure, compliant data exchange across systems
Say hello to your new agents
Our team of agents for clinical monitoring, TMF management, and PI and audit readiness, make work easier than ever before.
Roadmap to adopting AI agents
The successful integration of AI agents in enterprise operations requires a balanced, deliberate approach. Drawing from recent research in Strategic Integration (SI) and agentic AI adoption within large enterprises, the following best practices help maximize value, manage change effectively, and mitigate common pitfalls. Medable Agent Studio specifically streamlines this process by providing robust tools, no-code simplicity, and built-in compliance and security standards.
The agentic revolution for clinical trials
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The future economics of CROs
The ground has shifted for FSO (functional service outsourcing) and Unitized FSP (functional service providers) contract research organizations (CROs).
For years, the playbook for managing these types of CRO economics was familiar. Sponsors negotiated rates while CROs managed headcount and utilization around those rates .
That playbook still holds true today in theory. However, the conditions underneath it have shifted enough that it no longer produces the results it used to. Funding is tighter, sponsors are smaller and more price-sensitive, timelines are compressing ahead of the patent cliff, and AI has moved from an experiment on the roadmap to a baseline expectation in every RFP.
None of that is unique to any one segment of the market, but these pressures land differently for FSO and unitized FSP providers than they do for full-service CROs. That’s because these businesses are built on rate cards for CRAs, monitors, and other functional resources. When sponsors squeeze rates or expect more output per unit, there's no broader program fee to absorb the hit, here the unit economics are the business.
Rapid evolution: How agentic AI is redefining the role of the CRA
The way clinical trials are monitored is once again about to change.
This isn’t the first time clinical trial monitoring has evolved. Over the past three decades, the industry has undergone two major transformations in how monitoring is performed.
For much of the 1990s and early 2000s, monitoring relied on frequent on-site visits and extensive source data verification (SDV), with many studies aiming to verify nearly every data point. While rigorous in intent, this approach became increasingly difficult to sustain as trials grew larger and more complex, delivering diminishing returns relative to its cost and operational burden.
The industry responded by adopting risk-based monitoring (RBM) and centralized monitoring, shifting from exhaustive verification to a targeted, data-driven approach focused on the risks that mattered most. This evolution was reinforced by FDA and EMA guidance and ultimately codified in ICH E6(R2) in 2016.
While RBM improved efficiency and data quality, it did not fundamentally change how monitoring work was performed. CRAs still spent much of their time manually reviewing data, reconciling information across systems, documenting findings, and coordinating follow-up activities.
We are now entering a third shift. Unlike the first two, which primarily redistributed how monitoring effort was allocated, this one fundamentally changes the old rules on who and what is monitoring trial performance.
Frequently asked questions
Agentic AI refers to autonomous, goal-driven AI systems—often called “agents”—that can reason, plan, and act in complex environments with minimal human intervention. In clinical development, these agents can manage and optimize trial workflows such as protocol design, patient recruitment, site coordination, and regulatory documentation. Unlike traditional automation, which follows static rules, agentic AI adapts to new information, learns from trial progress, and proactively orchestrates tasks to keep studies on track and compliant.
AI agents help clinical trials run faster, more efficiently, and with greater quality. By automating repetitive tasks, adapting protocols in real time, and reducing human error, they streamline operations across sites while ensuring compliance and consistency. They also enable more patient-centric approaches by improving communication and engagement, ultimately making it easier to scale complex global trials with fewer resources.
Delivering trustworthy Agent recommendations requires two key elements:
The AI Model – We select and fine-tune models that are purpose-built for life sciences, trained on high-quality, relevant data. This ensures the model understands domain-specific language, context, and regulatory requirements, reducing the risk of inaccurate or irrelevant outputs.
The Agentic Environment & Verification Process – Agents operate in a controlled environment with rigorous validation checkpoints, business logic guardrails, and real-time monitoring. Every recommendation is subject to verification workflows and data quality checks before it’s surfaced to users, ensuring accuracy, compliance, and auditability.