Demo Session - Smart use of GenAI and ML to enable more efficient and real time adaptive clinical trials

Time management in clinical trials is the most challenging task by far, suffering from a high degree of randomness. The advent of precision medicine and targeted therapies has introduced an additional layer of complexity to the inclusion and exclusion criteria for the recruitment, which is the lengthiest stage whichever the clinical trial phase, as well as to the tracking of adverse events (AE) in enrolled patients.

In this demo we want to show how the integration of cloud technologies and artificial intelligence (AI) in healthcare has opened doors to innovation in terms of adaptability and efficiency. These technologies can redefine the way that clinical trials are designed and executed optimizing their entire lifecycle having a significant impact in all stages. We will show this impact in clinical data management, recruitment and site preselection.

The usage of Generative AI (GenAI) makes possible to structure information from the Electronic Health Record (EHR) within seconds, accelerating patient recruitment by matching patient’s history with eligible criteria within all available clinical trials. Moreover, those models can also be used within a clinical trial to automatically fill the electronic Case Report Form (eCRF), not only reducing the risk of human errors but also enabling real time monitoring from patients and enabling adaptive adjustment to protocols for improved decision-making moving towards patient centricity and focusing on the key endpoints contributing to maximize the value of the drug and to the success of the clinical trial.

Application of Machine Learning (ML) to public data, patient population and administrative data from previous experiences monitored through the corresponding Clinical Trial Management Systems and recorded in the electronic Trial Master File (CTMS/eTMF) is key to ensure the efficiency of clinical trials and control the risks of delays.

 Learning outcomes:

• Demonstration of GenAI applied to clinical data (EHR)
• Enhanced recruitment and patient centricity backed up with data
• Smart site preselection