Blog posts

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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For nearly six decades, the way data moves through a clinical trial has barely changed. A site collects it, a sponsor analyzes it, and the FDA eventually receives it, months or years after the fact. That lag has delayed regulatory decisions, slowed drug development timelines, and in some cases, kept promising therapies from reaching patients who needed them sooner.

Thankfully, that is starting to change. In late April 2026, the FDA announced something that has been talked about for years but rarely demonstrated, real-time clinical trials. 

The agency unveiled two proof-of-concept studies already underway, one with AstraZeneca and one with Amgen, where safety signals and endpoint data are being shared with the FDA as the trial progresses, not months or years later. Additionally, a broader pilot program is set to launch this summer.

This is a fundamental rethinking of how clinical evidence gets generated and reviewed, with implications for how quickly the industry can move from trial initiation to regulatory decision. The old model was sequential in how it collected data, packaged it, and delivered the data at the end. The new model treats it as something that flows continuously, in real time, to the people who need to act on it.

However, real-time data sharing only works if the data being shared is coherent in the first place. 

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What makes a good doctor? Being an expert in modern medicine is table stakes, as no one trusts a doctor who doesn't know the ins and outs of healthcare. 

However, as anyone who has encountered a medical professional whose bedside manner left something to be desired, it’s not enough. Research agrees, as studies from all the way back to the 1980s shows that knowledge without communication skills often fails to get patients to adhere to treatments.

The best doctors combine both clinical expertise and genuine bedside manner in order to give patients the best experience. 

This framing and duality is exactly what sponsors and contract research organizations (CROs) should keep in mind when searching for clinical trial AI. 

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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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Discover what to look for from artificial intelligence tools in the clinical trial market.

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Artificial intelligence is reshaping clinical trial operations in 2026. Sponsors and CROs are no longer evaluating standalone tools. They are choosing platforms that can connect with existing systems, automate complex workflows, and scale without disrupting ongoing studies. This guide breaks down the vendors best positioned to integrate with the clinical trial infrastructure you already have.

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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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Real-time clinical trials are here. Medable's continuous trial management platform gives sponsors the evidence generation, agentic AI, and regulatory-ready infrastructure to run clinical programs at the speed the FDA now expects.

Artificial Intelligence is rapidly transforming clinical research. From patient recruitment and protocol design to medical writing and data review, AI-powered solutions are becoming embedded across the clinical development lifecycle. More recently, the emergence of Agentic AI (systems capable of planning, reasoning, and executing multi-step workflows with varying degrees of autonomy) has generated significant excitement throughout the industry.

However, not all AI solutions are created equal. While many vendors promise dramatic improvements in efficiency and productivity, clinical trial organizations operate in one of the most highly regulated environments in the world. Success depends not only on technical performance but also on compliance, validation, governance, security, and trust.

As sponsors, CROs, and technology teams evaluate potential AI partners, they need a framework that extends beyond traditional software procurement criteria. The following considerations can help organizations assess both AI and Agentic AI vendors and identify solutions that are truly ready for clinical research.

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Oncology has never been in a better scientific position. Precision medicines, adaptive study designs, and biomarker-driven cohorts have opened up treatment possibilities that simply did not exist ten years ago. But the complexity of running these trials has grown at much the same pace as the science itself, and that gap between scientific ambition and operational capability is where many programmes quietly struggle.

For sponsors building oncology portfolios, and for the CROs executing them, the operational challenge is no longer a peripheral concern. Getting it right comes down to three things: being consistent enough to build efficiently, agile enough to adapt when the science changes, and scalable enough to manage a growing portfolio without the overhead growing at the same rate.

Medable has worked with sponsors and CROs across many global oncology programmes, spanning thousands of sites and participants. That experience has given us a clear picture of what separates programmes that move well from those that get stuck.

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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.