The old paint on one board has already dried, and the new coat hasn't been applied yet, but you need to know which one protects better against rain. Usually, you'd paint two boards and wait a year. The researchers did something different: they taught a computer to predict, from old photographs, how the same board would look under a different paint. No real experiment needed.

In medicine, this trick is called a virtual control. Instead of hunting for children with Crohn's disease who would agree to a year of treatment with an older drug, the scientists took medical records from those who had already received infliximab (IFX). On this data, they trained five machine learning models — five different ways to peek into the future. Then they asked each model: what would have happened to the patients who actually received adalimumab (ADA) if they had been given IFX?

This approach is especially valuable when assembling a control group is ethically impossible or too costly. A computer trained on historical data becomes a "digital twin" of an entire patient group. The model still needs validation on new data, but the first step is done: virtual helping hands are already reaching into clinical trials — almost like that board painted without a single brushstroke.