Applications of AI-generated digital twins to improve efficiency and decision making in clinical trials

Music City Center 

Effective and rapid decision-making in clinical trials requires unbiased and precise treatment effect inferences. Recent advances in artificial intelligence (AI) are poised to revolutionize breakthroughs in innovative trial design and analyses, improving efficiency in Phase 2 and 3. We present on AI-enabled methods that combine digital twins and traditional statistical frameworks to improve trial efficiency that satisfy regulatory guidance. Digital Twin Generators (DTG) are pre-trained generative models that generate digital twins for each trial participant using only baseline measurements and are fully prespecifiable. Digital twins are individualized, probabilistic distributions of a participant's disease progression and do not require any changes to trial conduct itself. Combined with traditional Frequentist and Bayesian frameworks, digital twins increase power and reduce sample size in trials without compromising Type 1 error control. Digital twins also improve internal decision making through personalized p-values for subgroup discovery and optimized composite scores. We present results of recent case studies and discuss prospective applications of these methodologies.

AI-generated digital twins