Clinical trials are critical for evaluating the safety and efficacy of new treatments, but they are expensive and time-consuming. AI can help by analyzing patient data to identify eligible patients, optimizing trial design to reduce costs and increase efficiency, and predicting patient outcomes to improve trial success rates.

The FDA has approved thousands of medications for pharma companies. These meds and the journeys of the patients that take them appear in the data upon which we have trained. Therefore our model is able to reproduce the interaction effects of those treatments with various patient cohorts. Instead of recruiting patients for clinical trials, you can now use generative AI to create patients with desired histories. And instead of administering drugs over multiple years in vivo, you can have the generative AI simulate the administration of those drugs in silicon compute.

The use of AI in clinical trial optimization has the potential to dramatically improve the efficiency and success rates of clinical trials, bringing new treatments to market faster and more efficiently. By leveraging AI to optimize trial design, identify eligible patients, and predict patient outcomes, healthcare organizations can improve patient outcomes while reducing costs.