Intelligent Acceleration: Redefining Clinical Trials for Drugs and Devices with AI

Abstract

Clinical trials face persistent inefficiency, with drug development spanning 10-15 years, device studies requiring 3-7 years, and annual costs exceeding $230 billion globally. Artificial intelligence is fundamentally transforming clinical research through predictive analytics, automation, data integration, and operational optimization. This mini review synthesizes regulatory guidance and empirical evidence from 2020-2025 to examine how AI applications accelerate both drug and device trials and proposes a strategic implementation framework. We conducted systematic searches in PubMed, arXiv, Scopus, and regulatory archives (FDA, EMA) using structured search strings. From approximately 450 initial publications, key sources were selected following rigorous inclusion criteria emphasizing empirical validation and clinical relevance. We developed a four-pillar framework integrating: (1) Predictive Intelligence using machine learning for outcome forecasting; (2) Automation through natural language processing and robotic process automation; (3) Integration of R&D platforms and regulatory systems; and (4) Transparency via explainable AI for regulatory trust. Evidence demonstrates AI can reduce trial duration by 25-40%, lower operational costs by 30%, and double recruitment speed while improving safety monitoring and patient retention. A case study from Cleveland Clinic demonstrated AI-powered patient identification in approximately 2.5 minutes with 96% accuracy, compared to 427-540 minutes for manual screening with 88- 95% accuracy. When deployed within robust governance structures and internationally coordinated regulatory frameworks, AI integration represents a paradigm shift enabling faster, safer, and more efficient clinical development. Implementation challenges include data standardization barriers, algorithm validation concerns, and the critical need for harmonized global infrastructure to realize AI's transformative potential.

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