Abstract

REVOLUTIONIZING CLINICAL RESEARCH WITH ARTIFICIAL INTELLIGENCE: STRATEGIES AND APPLICATIONS

Gautham J. B.* and Sudhamshu K. Tantry

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Abstract

The integration of artificial intelligence (AI) into clinical research is revolutionizing traditional methodologies, enhancing efficiency, and improving patient outcomes. This review explores the diverse applications of AI throughout the clinical research continuum, from preclinical studies to post-market surveillance. AI plays a crucial role in preclinical research by employing machine learning algorithms to identify potential drug candidates and optimize experimental designs, significantly reducing time and costs. In the clinical trial phase, AI facilitates patient recruitment through predictive analytics that identify suitable participants based on eligibility criteria, demographics, and historical data. AI-driven tools enhance patient retention strategies by enabling personalized communication and monitoring, ensuring adherence to protocols and improving trial outcomes. The integration of AI in clinical trial conduct highlights automated data collection, real-time monitoring, and adaptive trial designs that allow for dynamic modifications based on interim results. Advancements in clinical data management, enabled by AI technologies, are transforming data handling processes. Software solutions such as Oracle Clinical, SAS Clinical, and OpenClinica streamline data entry and analysis, ensuring data integrity and compliance with regulatory standards. Despite numerous benefits, challenges remain in the widespread implementation of AI in clinical research. Issues such as data privacy concerns, the need for robust validation processes, and regulatory frameworks must be addressed to facilitate the adoption of AI technologies. The potential of AI to transform clinical research is immense, offering innovative solutions to improve methodologies and patient care.

Keywords: Artificial intelligence, Clinical trial, Clinical research.