Enroll Patients Months Ahead of Schedule

Accelerating the Path from Discovery to Patient

Clinical trial design with AI enables simulation of trial outcomes under different design parameters, sample sizes, endpoint selection, patient stratification strategies, and adaptive design triggers. AI simulation can identify optimal adaptive trial designs that reduce sample size requirements while maintaining statistical power, directly reducing the cost and duration of clinical trials that average $1-2 billion and 10-15 years for new drug approval.

Patient recruitment, historically one of the biggest drivers of clinical trial delays and failures, benefits from AI that identifies eligible patients from electronic health records, matches patients to trials based on complex inclusion/exclusion criteria, and predicts patient dropout risk. AI-powered recruitment has reduced site activation time and enrollment periods significantly in multiple large trials.

Biomarker discovery using AI identifies patient subpopulations most likely to respond to a therapy, enabling precision medicine approaches where a drug that fails in a broad population may achieve regulatory approval in a biomarker-defined subgroup. AI analyzis of genomic, proteomic, imaging, and clinical data surfaces predictive biomarkers that would not be identifiable through traditional statistical approaches in standard clinical datasets.

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