The Convergence of Artificial Intelligence and Cloud-Native Architectures in Pharmacovigilance: Revolutionizing Real-Time Patient Monitoring and Therapeutic Development
Keywords:
Pharmacovigilance, Artificial Intelligence, AWS Lake House, Clinical TrialsAbstract
The pharmaceutical and healthcare sectors are currently navigating a transformative epoch defined by the integration of Artificial Intelligence (AI), Machine Learning (ML), and cloud-native data architectures. This research article explores the systemic transition from traditional, retrospective pharmacovigilance to real-time, predictive therapeutic monitoring. By synthesizing current FDA perspectives on AI in drug development with the implementation of AWS Lake House architectures, this study delineates a framework for enhancing drug safety and clinical trial efficacy. We investigate the role of AI in therapeutic target discovery, the automation of adverse event detection, and the optimization of patient-reported outcomes (PROs) through digital diary compliance. Central to this investigation is the challenge of data siloization and the potential for "Lake House" structures to provide the low-latency analytics required for acute clinical alerting. The article elaborates on the psychometric evaluation of digital endpoints, the regulatory landscape for AI-driven decision support, and the theoretical implications of continuous monitoring in diverse patient populations. Findings suggest that the synergy between AI-powered predictive analytics and scalable cloud infrastructure not only reduces the burden on patients but also accelerates the transition from concept to clinic, ensuring a data-driven future for global healthcare.
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