AI

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. 

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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.

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With 44,000+ oncology professionals in attendance, ASCO 2026 may have been the most consequential in recent years. From a standing ovation for pancreatic cancer to the first positive sarcoma trial in history, here's everything that mattered at Chicago's McCormick Place this week.

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Medable is a Palo Alto-based platform that has positioned itself as a leader specifically in decentralized clinical trials (DCT), eCOA, and — most recently — agentic AI. It has been deployed in nearly 400 trials across 70 countries and 120 languages, serving more than one million patients globally, and has been recognized as a Leader in eCOA by Everest Group.

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Clinical data speaks dozens of languages. Ontologies are the translator. Discover how life sciences teams are using semantic layers, AI agents, and MCP connectors to cut months of data harmonization down to days.

AI-powered remote patient monitoring (RPM) is transforming clinical trials by enabling continuous data collection, real-time insights, and decentralized participation. This ecosystem spans wearables, AI analytics, data platforms, and decentralized clinical trial (DCT) infrastructure.

Additionally, agentic AI is fundamentally reshaping remote patient monitoring (RPM) in clinical trials by shifting it from passive data collection to proactive, autonomous decision support. Instead of simply aggregating data from wearables and patient-reported outcomes, agentic systems can continuously analyze multi-source trial data, identify emerging risks, and take action, such as prioritizing at-risk patients or sites, triggering alerts, or recommending interventions, without waiting for human input. This significantly reduces delays in detecting safety signals or protocol deviations. Just as importantly, agentic AI introduces workflow automation at scale by handling routine monitoring tasks, coordinating communications, and maintaining audit-ready reasoning trails. The result is a more adaptive and responsive RPM model where clinical teams move from manual oversight to strategic supervision, enabling faster, safer, and more efficient trials.

Below is a structured overview of the leading vendors, tools, and providers enabling AI-driven RPM in clinical research.

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Medable’s clinical trial platform leverages a robust network of connectors to integrate seamlessly with the systems that power study execution, from EDC and CTMS to collaboration and data platforms. These connectors enable AI to operate across workflows in real time, unifying data, automating processes, and improving coordination across team

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Let’s ask a trick question. 

Do you think your organization’s data is ready for AI, or AI Agents?

Most sponsors and CROs instinctively answer “not yet.” What this really means is that they don’t believe their data isn’t fully centralized, dictionaries aren’t perfectly aligned, and too many systems still operate in parallel. The result is that AI gets parked on the roadmap, waiting for a future state where everything is clean, standardized, and coordinated.

Here’s the twist; waiting for that moment is very thing holding organizations back.

When it comes to implementing agentic AI, the bigger risk right now isn’t imperfect data. Instead, it’s waiting for perfection before acting.

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To many, the SCOPE Summit is the year’s “newsroom,” setting the stage for what hot topics and driving forces will dominate the coming year. 

With this year’s conference winding down, we’re once again offering a glimpse into the evolving operational and technological conversations shaping the future of trials with our recap below.  

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eCOA has moved from a supporting tool to a foundational pillar of modern clinical trials, and Everest Group agrees. In its inaugural eCOA Products PEAK Matrix Assessment, Everest named Medable a Leader, citing strong market impact, accelerated timelines, and a platform built for real-world trial complexity. As the eCOA market surges toward nearly $1B in value, this recognition underscores how speed, patient experience, and AI-driven innovation are reshaping how trials are designed, launched, and scaled globally.

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“Big corporations can’t rely on their internal speed to match the transformation that is happening in the world. As soon as I know a competitor has decided to build something itself, I know it has lost.” 

These candid sentences from Sanofi CEO, showcase one of the most common questions that’s at the forefront of every pharmaceutical company’s mind; whether to build or buy your way into the agentic and generative AI revolutions. 

In life sciences, many teams start with the same instinct. They see a capable large language model, stand up a proof of concept, and feel close to a breakthrough. For most of us, AI prototypes can look magical. A chatbot summarizes visit reports, drafts emails, or answers protocol questions in minutes. The experience is so strong that teams assume production is a short step away.

Unfortunately, the gap is much bigger than it looks. 

According to a recent MIT study, 95% of AI pilots will fail, as they note that “Only 5% of custom GenAI tools survive the pilot-to-production cliff, while generic chatbots hit 83% adoption for trivial tasks but stall the moment workflows demand context and customization.”

Like MIT’s example shows, moving from prototype to production in clinical research means building something validated, compliant, scalable, and integrated into real workflows. That takes far more than clever prompts. It requires domain grounding, continuous monitoring, retraining loops, robust tool orchestration, and evidence that the system is safe and auditable under regulations like GxP, HIPAA, and 21 CFR Part 11.

Many organizations only discover the hidden costs after they have committed. Internal teams often invest for two years, spend millions in sunk cost, and still never reach a dependable clinical grade system. The illusion comes from how easy it is to get an early demo working, and how hard it is to make that demo survive contact with trial reality. 

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