Your co-pilot for clinical monitoring
Medable’s Clinical Monitoring Agent is an AI-driven solution that automates and optimizes clinical trial monitoring by proactively identifying and prioritizing site risks, generating comprehensive pre-visit summaries, and providing actionable recommendations to enhance trial oversight and compliance.
We estimate that CRA agents can take on up to 90% of the tactical and administrative work the Clinical Monitoring handles on a daily basis — from sending site-specific emails and reminders to tracking responses and updating systems. That means we can focus our time on the strategic decisions that move trials forward, while standardizing and streamlining the tasks that used to consume most of our day.
Connected monitoring across systems
Perceive signals across the entire clinical data ecosystem by unifying CTMS, RTSM, EDC, labs, consent, and safety into one view. Turn manual checks into automated insights, instantly highlighting which sites are on track and where intervention is needed, saving valuable time, system-hopping, and manual data compilation.
Human-in-the-loop oversight
Easily identify risks and recommendations for best-next actions tied to your protocol, consent, and regulatory requirements. Stay assured with a transparent reasoning trail thatshows why each step is advised, enabling CRAs to collaborate confidently with sites, improve decision-making, enhance their compliance, all while freeing CRAs to focus on engagement and site-specific challenges.
Automated administrative tasks
Take advantage of automated routine tasks like drafting queries, sending emails, and updating CTMS/eTMF — always with human CRA oversight. Close the loop between planning and execution with real-time tracking that reduces administrative burden, ensures data consistency, and accelerates trial progress.
The latest from Knowledge Centers
The biggest misconceptions about agentic AI readiness
To say there’s movement within the agentic life sciences market would be an understatement. According to seven different market research organizations, the “Agentic AI in clinical trials market” is expected to grow at a compound annual growth rate (CAGR) of anywhere between 12.5% and 43%.
While many sponsors and CROs surveyed want agentic AI operating at some level within their clinical trials, almost none of them think they're ready for it. "Our systems don't talk to each other." "Our data is a mess." "We need a two-year foundation project before we can even think about agents." These aren't fringe concerns, they're the default assumptions in nearly every boardroom conversation about AI adoption.
Here's the problem. Those assumptions are very wrong, and they're costing sponsors real time. While teams wait for the "right" conditions to start, the gap between early movers and everyone else keeps widening. The truth is, readiness isn't a prerequisite for agentic AI, it's a byproduct of starting.
Below, we take on the myths that keep organizations stuck in planning mode, and the facts that show why the window to start is now, not after your data is perfectly clean and your stack is fully unified.
Is Data Readiness Slowing Down AI in Clinical Trials? How Agentic AI Enables Immediate Impact
Most sponsors and CROs say their data isn't ready for AI. But agentic AI does not require a perfect data environment to begin delivering value. It can be deployed compliantly across siloed platforms, interpreting and reconciling differences in real time. In this 60-minute session, learn how AI agents deliver measurable value safely across clinical trials, reduce cognitive and operational burden, and enable teams to generate impact now while strengthening data foundations over time.
What to look for in an AI clinical trial platform: A buyer's guide
Discover what to look for from artificial intelligence tools in the clinical trial market.
Eliminate clinical trial white space with the right AI strategy
It has become clear that our industry has reachedthe limits of human-only clinical development. As clinical trials have become increasingly complex, the endeavors that people alone can perform are no longer sufficient to generate the momentum needed to address the growing burden of human disease. This has led to longer drug development timelines and significant delays for patients. One large are of lost time is “white space,” definied simply as unproductive time caused by manual, sequential processes and fragmented data systems. Thankfully, a solution lies in agentic AI and its abilities to perform series of tasks.