Artificial Intelligence and Machine Learning Innovations to Improve Design and Representativeness in Oncology Clinical Trials

Abstract

The integration of artificial intelligence (AI) and machine learning (ML) in oncology clinical trials is rapidly evolving alongside the broader field. For example, AI-driven adaptive trial designs may allow for real-time modifications based on emerging safety and efficacy signals, enabling more responsive and efficient trials. AI-powered diagnostic tools, including radiomics, computational pathology, and spatial omics, can improve trial patient selection and response assessments. ML-based patient outcome simulations can similarly enhance patient stratification strategies and statistical power. Application of AI can also improve the accessibility of real-world data, including opportunities to enhance data extraction, standardization, and harmonization of data from routine clinical practice. Data generated from digital health technologies (eg, wearable devices, electronic sensors, computing platforms, software applications) may enable a more comprehensive understanding of patient populations to support clinical trials from enrollment to assessment. Automation of trial operations and data management can also improve data fidelity and decrease investigator burden, which has the potential to streamline trial execution and increase potential use of decentralization. There are ongoing efforts to enhance regulatory clarity, mitigate bias, and uphold ethical use of these novel technologies. In this article, we review use cases of AI and ML in oncology clinical trials, including their role in patient recruitment, trial design and operations, data management, and diagnostics. Although these technologies can have applications across all phases of drug development including early discovery, we focus on phase II and III trials, where AI and ML may have a pronounced ability to enhance trial efficiency, patient stratification, and regulatory decision making. By integrating AI and ML, clinical trials can become more adaptive, data-driven, and inclusive in the pursuit of improving patient outcomes.

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