Growing Pains: The Real Story Behind Gen AI Development In Clinical Trial Ops

By Christina Dinger, Sr. Director of Product, ThoughtSphere

The development of Generative AI (Gen AI) in clinical trials has been likened to a progressive journey from infancy to maturity. As Gen AI reaches advanced stages, it also introduces risks such as model hallucinations, which can affect the accuracy and reliability of trial outcomes. To address these challenges, the implementation of safeguards is crucial. Techniques like Retrieval-Augmented Generation (RAG) and Human-in-the-Loop (HITL) checkpoints are essential for ensuring the accuracy and reliability of AI outputs. Continuous retraining of AI models is also necessary to prevent biases and model drift, ensuring that the technology remains aligned with clinical objectives.

The evolution of Gen AI requires patience and the application of rigorous quality measures. ThoughtSphere, a leader in this field, emphasizes the importance of refining AI models and balancing automation with human oversight. This approach not only enhances the reliability of clinical trials but also ensures that AI technologies are developed responsibly, ultimately benefiting the healthcare industry.