The FDA recently issued a request for information on its AI-Enabled Optimization of Early-Phase Clinical Trials Pilot Program, part of a broader push toward real-time clinical trials. We filed comments on the public docket, authored by our Chief Scientific Officer, Jonathan Walsh. Here are our main takeaways: 

Two use cases for how AI can sharpen early-phase trial decisions

The first use case begins with digital twins as efficacy benchmarks. A digital twin is an AI-generated prediction of a trial participant’s comprehensive clinical outcomes on control or standard of care. Digital twins are generated for each enrolled participant from their unique baseline data; thus, the resulting benchmark reflects the actual study population rather than a historical average. It is available in near real time, and because it sits alongside the pre-specified analysis rather than replacing it, it gives reviewers and sponsors a sharper reference for interpreting an early signal. It does not stand in for a randomized control, or for the confirmatory analysis when the science calls for one. Its role is narrower, and still valuable: it helps teams make more informed go/no-go decisions earlier.

The same predictions deliver a second benefit, one that bears on trial quality rather than efficacy. Once every participant has a predicted trajectory (their digital twin), the observed data can be compared against it, and when deviations cluster at a particular site or vendor, they often provide the first visible sign of an operational or data problem. Issues like these typically surface only at database lock. Measured against the model, they can appear weeks or months earlier, serving as an advisory flag that prompts a closer look.

The second use case scales the same idea up, from a twin of each patient to a twin of the entire trial. As data arrive from Phase 1 or early Phase 2, the model recalibrates against them and becomes a simulation engine for designing the next phase before the current one has closed. Questions that normally wait for a final readout can be addressed in silico: which population to carry forward, which endpoint to use, how long to follow patients, and how large the next study needs to be. Because the model draws on both the historical data and the trial as it stands, those answers reflect the population and treatment behavior actually observed so far. The result is a shorter gap between phases, which is the central aim.

AI Adoption is here. Doing it well is a choice.

AI is already being adopted across clinical development, and that isn’t likely to change. The real question is no longer whether to use it, but how to use it well, and answering that requires the FDA at the table from the beginning, not as a reviewer at the end. That is what makes the pilot's design so important. If it moves too fast, it risks the trust the whole effort depends on. If it waits for the field to adopt these methods on its own, the delay could stretch into decades, with patients paying the cost. A good pilot avoids both traps: it sets clear rules up front for how trial data will be interpreted, and it meets sponsors at their level of technical experience instead of shutting out those still building it.

We have standing to say this because we have done it. Since 2017, we have taken our method for using AI-generated digital twins in clinical trials the full distance from research results to regulatory adoption, with EMA qualification in 2022 and supportive guidance from the FDA in 2023. Our position has held throughout: these tools give expert teams better evidence, and the teams still make the call. A focused pilot on digital-twin benchmarks and live trial simulation can deliver near-term gains in early-phase decisions while laying groundwork for the wider use of AI that outlasts the pilot itself. Our full comments are on the public docket, and if these questions are live in your own early-phase programs, we would welcome the conversation.