Articles
| Open Access | The Integration of Explainable Artificial Intelligence and Decentralized Frameworks in Clinical Research: A Comprehensive Analysis of Methodological Shifts and Ethical Governance
Dr. Alistair Sterling , Department of Biomedical Informatics and Health Outcomes, University of EdinburghAbstract
The landscape of clinical trials is undergoing a fundamental paradigm shift driven by the convergence of Artificial Intelligence (AI), Machine Learning (ML), and decentralized operational models. This research article explores the transition from traditional, site-centric randomized clinical trials to digital, participant-centric models enabled by remote monitoring and predictive analytics. By synthesizing foundational work on model interpretability, such as SHAP (SHapley Additive exPlanations), with contemporary frameworks for decentralized clinical trials (DCTs), this study investigates how AI-driven strategies can enhance trial efficiency while maintaining scientific rigor. We examine the critical role of explainable AI (XAI) in gaining clinician trust and meeting regulatory requirements, particularly in the context of real-time monitoring and adaptive design. The paper further discusses the necessity of pragmatism in trial design, utilizing the PRECIS-2 tool and real-world data to bridge the gap between controlled experimental efficacy and real-world clinical effectiveness. Finally, we address the ethical imperatives of equity, diversity, and inclusion (EDI) within AI-enabled trials, arguing that algorithmic interventions must be intentionally designed to mitigate bias and broaden participant access.
Keywords
Decentralized Clinical Trials, Explainable Artificial Intelligence, Remote Patient Monitoring, Adaptive Design
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Copyright (c) 2026 Dr. Alistair Sterling
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