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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