Cutting Clinical Trial Delays with Secure, AI-Driven Prescreening
Blog post from Duality
Pharmaceutical companies are increasingly turning to AI-driven prescreening models, powered by privacy-enhancing technologies (PETs), to address delays in clinical trial enrollment due to the challenges of patient prescreening. These models can predict a patient's likelihood of meeting trial eligibility criteria without moving or exposing sensitive data, thanks to technologies like Trusted Execution Environments (TEEs) and Federated Inference. By keeping patient data secure and running models locally, PETs enable faster and more efficient prescreening processes, reducing screen-failure rates and shortening the time between referral and enrollment. This approach is transforming trial operations from a reactive to a proactive model, allowing for quicker trial startups and enhanced patient diversity while ensuring compliance with data privacy regulations. A global pharmaceutical company has already demonstrated the effectiveness of this approach by implementing PETs to enhance biomarker prediction across diverse data sources without compromising patient privacy or model integrity, marking a significant shift towards more efficient and trusted clinical trial processes.
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