AI and Innovation in Clinical Trials

Clinical Trials (CTs) are the gold standard in evidence-based medicine, but their successful design and implementation face several challenges. Substantial operational costs associated with a well-executed CT are a barrier to entry for many therapies. Despite feasibility efforts, many trials struggle with accrual. Elaborate protocols and stringent eligibility criteria impede the efficiency of participant identification and recruitment. For trials that do complete enrollment, patient cohorts may not be representative of the broader real-world patient populations that may benefit from the trial treatments. Moreover, the public’s perception of CTs—complicated by the complex terminology and schedule of events—can lead to hesitancy in enrolling or maintaining adherence. Thus, there is a pressing need for innovative solutions. Advances in Artificial Intelligence (AI) algorithms, including machine learning (ML) and Large-Language Models (LLMs), together with high-performance computing, hold significant potential to disrupt the status quo.