Enhancing Outcome Measurement in Oncology Clinical Trials Through Artificial Intelligence: A Scoping Review

The phased framework of oncology trials is designed to ensure patient safety and conserve resources by advancing only promising therapies from early- to late-phase testing. Despite decades of refinement, overall trial success rates-defined by the proportion of studies ultimately supporting regulatory approval-remain low, with failures increasingly occurring in late-phase studies. These failures are often contributed to by methodological shortcomings, including suboptimal end point selection, restrictive eligibility criteria, and inefficient trial designs. Although traditional approaches to biomarker discovery, outcome validation, and eligibility refinement have yielded transformational advances, increasing molecular subclassification of tumors into rare subgroups results in the conventional drug development framework being no longer fit for purpose. Artificial intelligence (AI) offers opportunities to enhance the efficiency, precision, and patient-centeredness of oncology trials. Deep learning systems integrate and analyze large data sets to uncover complex patterns often inaccessible to conventional methods. AI has potential applications in patient-trial matching, optimization of eligibility criteria, statistical modeling of survival outcomes, and the identification of novel surrogate end points although these applications remain largely investigational and are not yet established for routine use. This scoping review provides a structured overview of AI applications in oncology trials, with emphasis on outcome selection and surrogate end point evaluation. We also highlight emerging areas with potential for immediate implementation, such as patient selection, biomarker identification and synthetic control arms, to accelerate development and enhance clinical care. However, broader harmonization is needed to ensure reproducibility, transparency, and regulatory confidence before implementation. Ultimately, early and sustained collaboration between trialists, AI developers, and regulators will be essential to ensure that AI delivers meaningful advances in the design, evaluation, and delivery of new medicines.