Engineering and Technology
| Open Access | Transforming Clinical Research Paradigms: An Integrated Framework for AI-Driven Patient Recruitment, Virtual Trial Architectures, and the Promotion of Health Equity
Dr. Elena Vance , Center for Health Informatics and Systems Research, University of Toronto, CanadaAbstract
The traditional landscape of clinical research is currently undergoing a fundamental shift, moving from centralized, resource-heavy methodologies to agile, decentralized, and data-centric frameworks. This transformation is necessitated by escalating costs, suboptimal participant recruitment, and a persistent lack of demographic representation in clinical cohorts. This article provides a comprehensive investigation into the integration of Artificial Intelligence (AI) and Machine Learning (ML) as primary catalysts for this evolution. We examine the comparative efficacy of AI-powered trial matching against manual Electronic Medical Record (EMR) screening, the operational economics of virtual clinical trials as evidenced by pioneering decentralized studies, and the emergence of digital biomarkers as tools for continuous monitoring. Furthermore, this research addresses the critical imperative of Equity, Diversity, and Inclusion (EDI), analyzing how algorithmic strategies can mitigate historical biases and satisfy evolving regulatory mandates for racial and ethnic representation. By synthesizing evidence from oncology clinical decision support tools and big data analytics, this study posits a new theoretical model for the "Intelligent Trial"-a framework that optimizes site selection, enhances patient retention through digital consenting, and leverages real-world data to bridge the gap between experimental results and public health outcomes.
Keywords
Artificial Intelligence, Decentralized Clinical Trials, Patient Recruitment, Digital Biomarkers
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Copyright (c) 2026 Dr. Elena Vance
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