In Silico Trial Simulation with Artificial Intelligence-Generated Synthetic Control Cohorts Reproduces Results of a Randomized Controlled Trial in Acute Myeloid Leukemia

Abstract Rising costs, slow accrual and molecular sub-stratification of cancers necessitate novel clinical trial designs. We demonstrate that artificial intelligence-generated synthetic patients can replace real controls to reproduce results of the SORAML trial. Using external multimodal data from 1,377 acute myeloid leukemia (AML) patients from previous trials and a real-world registry, we fine-tuned a tabular foundation model to generate synthetic patients, reproducing clinical and genetic features and outcome associations. Synthetic patients were then matched to the original SORAML intervention group using Cox risk scores, replacing the original control and reproducing the original trial result with near-identical median event-free survival (EFS) and treatment effect (original hazard ratio [HR] 0.64, 95%-confidence interval [CI] 0.47–0.87, p =0.004; with synthetic control HR 0.66, 95%-CI 0.48– 0.90, p =0.009). Our findings demonstrate that AI-generated synthetic patients can serve as statistically rigorous controls supporting novel trial designs.